Executing commands from a distributed execution model

ABSTRACT

Systems and methods are disclosed for generating a distributed execution model with untrusted commands. The system can receive a query, and process the query to identify the untrusted commands. The system can use data associated with the untrusted command to identify one or more files associated with the untrusted command. Based on the files, the system can generate a data structure and include one or more identifiers associated with the data structure in the distributed execution model. The system can distribute the distributed execution model to one or more nodes in a distributed computing environment for execution.

RELATED APPLICATIONS

Any application referenced herein is hereby incorporated by reference inits entirety. Any and all applications for which a foreign or domesticpriority claim is identified in the Application Data Sheet as filed withthe present application are incorporated by reference under 37 CFR 1.57and made a part of this specification. This application is acontinuation of U.S. patent application Ser. No. 16/851,979, filed onApr. 17, 2020, entitled EXECUTING UNTRUSTED COMMANDS FROM A DISTRIBUTEDEXECUTION MODEL, which is a continuation of U.S. patent application Ser.No. 15/714,424, filed on Sep. 25, 2017, entitled GENERATING ADISTRIBUTED EXECUTION MODEL WITH UNTRUSTED COMMANDS, each of which isincorporated herein by reference in its entirety.

The present application also incorporates by reference the followingU.S. patent application in its entirety: U.S. application Ser. No.15/714,133, filed on Sep. 25, 2017, entitled EXECUTING A DISTRIBUTEDEXECUTION MODEL WITH UNTRUSTED COMMANDS.

FIELD

At least one embodiment of the present disclosure pertains to one ormore tools for facilitating searching and analyzing large sets of datato locate data of interest.

BACKGROUND

Information technology (IT) environments can include diverse types ofdata systems that store large amounts of diverse data types generated bynumerous devices. For example, a big data ecosystem may includedatabases such as MySQL and Oracle databases, cloud computing servicessuch as Amazon web services (AWS), and other data systems that storepassively or actively generated data, including machine-generated data(“machine data”). The machine data can include performance data,diagnostic data, or any other data that can be analyzed to diagnoseequipment performance problems, monitor user interactions, and to deriveother insights.

The large amount and diversity of data systems containing large amountsof structured, semi-structured, and unstructured data relevant to anysearch query can be massive, and continues to grow rapidly. Thistechnological evolution can give rise to various challenges in relationto managing, understanding and effectively utilizing the data. To reducethe potentially vast amount of data that may be generated, some datasystems pre-process data based on anticipated data analysis needs. Inparticular, specified data items may be extracted from the generateddata and stored in a data system to facilitate efficient retrieval andanalysis of those data items at a later time. At least some of theremainder of the generated data is typically discarded duringpre-processing.

However, storing massive quantities of minimally processed orunprocessed data (collectively and individually referred to as “rawdata”) for later retrieval and analysis is becoming increasingly morefeasible as storage capacity becomes more inexpensive and plentiful. Ingeneral, storing raw data and performing analysis on that data later canprovide greater flexibility because it enables an analyst to analyze allof the generated data instead of only a fraction of it.

Although the availability of vastly greater amounts of diverse data ondiverse data systems provides opportunities to derive new insights, italso gives rise to technical challenges to search and analyze the data.Tools exist that allow an analyst to search data systems separately andcollect results over a network for the analyst to derive insights in apiecemeal manner. However, UI tools that allow analysts to quicklysearch and analyze large set of raw machine data to visually identifydata subsets of interest, particularly via straightforward andeasy-to-understand sets of tools and search functionality do not exist.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and notlimitation, in the figures of the accompanying drawings, in which likereference numerals indicate similar elements and in which:

FIG. 1A is a block diagram of an example environment in which anembodiment may be implemented;

FIG. 1B is a block diagram of an example networked computer environment,in accordance with example embodiments;

FIG. 2 is a block diagram of an example data intake and query system, inaccordance with example embodiments;

FIG. 3 is a block diagram of an example cloud-based data intake andquery system, in accordance with example embodiments;

FIG. 4 is a block diagram of an example data intake and query systemthat performs searches across external data systems, in accordance withexample embodiments;

FIG. 5A is a flowchart of an example method that illustrates howindexers process, index, and store data received from forwarders, inaccordance with example embodiments;

FIG. 5B is a block diagram of a data structure in which time-stampedevent data can be stored in a data store, in accordance with exampleembodiments;

FIG. 5C provides a visual representation of the manner in which apipelined search language or query operates, in accordance with exampleembodiments;

FIG. 6A is a flow diagram of an example method that illustrates how asearch head and indexers perform a search query, in accordance withexample embodiments;

FIG. 6B provides a visual representation of an example manner in which apipelined command language or query operates, in accordance with exampleembodiments;

FIG. 7A is a diagram of an example scenario where a common customeridentifier is found among log data received from three disparate datasources, in accordance with example embodiments;

FIG. 7B illustrates an example of processing keyword searches and fieldsearches, in accordance with disclosed embodiments;

FIG. 7C illustrates an example of creating and using an inverted index,in accordance with example embodiments;

FIG. 7D depicts a flowchart of example use of an inverted index in apipelined search query, in accordance with example embodiments;

FIG. 8A is an interface diagram of an example user interface for asearch screen, in accordance with example embodiments;

FIG. 8B is an interface diagram of an example user interface for a datasummary dialog that enables a user to select various data sources, inaccordance with example embodiments;

FIG. 9 is an interface diagram of an example report generation userinterface, in accordance with example embodiments;

FIG. 10 is an interface diagram of an example report generation userinterface, in accordance with example embodiments;

FIG. 11A is an interface diagram of an example report generation userinterface, in accordance with example embodiments;

FIG. 11B is an interface diagram of an example report generation userinterface, in accordance with example embodiments;

FIG. 11C is an interface diagram of an example report generation userinterface, in accordance with example embodiments;

FIG. 11D is an interface diagram of an example report generation userinterface, in accordance with example embodiments;

FIG. 12 is an interface diagram of an example report generation userinterface, in accordance with example embodiments;

FIG. 13 is an interface diagram of an example report generation userinterface, in accordance with example embodiments;

FIG. 14 is an interface diagram of an example report generation userinterface, in accordance with example embodiments;

FIG. 15 is an interface diagram of an example report generation userinterface, in accordance with example embodiments;

FIG. 16 is an example search query received from a client and executedby search peers, in accordance with example embodiments;

FIG. 17A is an interface diagram of an example user interface of a keyindicators view, in accordance with example embodiments;

FIG. 17B is an interface diagram of an example user interface of anincident review dashboard, in accordance with example embodiments;

FIG. 17C is a tree diagram of an example a proactive monitoring tree, inaccordance with example embodiments;

FIG. 17D is an interface diagram of an example a user interfacedisplaying both log data and performance data, in accordance withexample embodiments;

FIG. 18 is a system diagram illustrating a data fabric service systemarchitecture (“DFS system”) in which an embodiment may be implemented;

FIG. 19 is an operation flow diagram illustrating an example of anoperation flow of a DFS system according to some embodiments of thepresent disclosure;

FIG. 20 is an operation flow diagram illustrating an example of aparallel export operation performed in a DFS system according to someembodiments of the present disclosure;

FIG. 21 is a flow diagram illustrating a method performed by the DFSsystem to obtain time-ordered search results according to someembodiments of the present disclosure;

FIG. 22 is a flow diagram illustrating a method performed by a dataintake and query system of a DFS system to obtain time-ordered searchresults according to some embodiments of the present disclosure;

FIG. 23 is a flow diagram illustrating a method performed by nodes of aDFS system to obtain batch or reporting search results according to someembodiments of the present disclosure;

FIG. 24 is a flow diagram illustrating a method performed by a dataintake and query system of a DFS system in response to a reportingsearch query according to some embodiments of the present disclosure;

FIG. 25 is a system diagram illustrating a co-located deployment of aDFS system in which an embodiment may be implemented;

FIG. 26 is an operation flow diagram illustrating an example of anoperation flow of a co-located deployment of a DFS system according tosome embodiments of the present disclosure;

FIG. 27 is a cloud based system diagram illustrating a cloud deploymentof a DFS system in which an embodiment may be implemented;

FIG. 28 is a flow diagram illustrating an example of a method performedin a cloud-based DFS system according to some embodiments of the presentdisclosure;

FIG. 29 is a flow diagram illustrating a timeline mechanism thatsupports rendering search results in a time-ordered visualizationaccording to some embodiments of the present disclosure;

FIG. 30 illustrates a timeline visualization rendered on a GUI in whichan embodiment may be implemented;

FIG. 31 illustrates a selected bin of a timeline visualization and thecontents of the selected bin according to some embodiments of thepresent disclosure.

FIG. 32 is a flow diagram illustrating services of a DFS systemaccording to some embodiments of the present disclosure;

FIG. 33 is a system diagram illustrating an environment for ingestingand indexing data, and performing queries on one or more datasets fromone or more dataset sources;

FIG. 34 is a block diagram illustrating an embodiment of multiplemachines, each having multiple nodes;

FIG. 35 is a diagram illustrating an embodiment of a DAG;

FIG. 36 is a block diagram illustrating an embodiment of partitionsimplementing various search phases of a DAG;

FIG. 37 is a data flow diagram illustrating an embodiment ofcommunications between various components within the environment toprocess and execute a query;

FIG. 38 is a flow diagram illustrative of an embodiment of a routine toprovide query results;

FIG. 39 is a flow diagram illustrative of an embodiment of a routine toprocess a query;

FIG. 40 is a flow diagram illustrative of an embodiment of a routine togenerate a query processing scheme;

FIG. 41 is a flow diagram illustrative of an embodiment of a routine toexecute a query on data from multiple dataset sources;

FIG. 42 is a flow diagram illustrative of an embodiment of a routine toexecute a query on data from an external data source;

FIG. 43 is a flow diagram illustrative of an embodiment of a routine toexecute a query based on a dataset destination;

FIG. 44 is a flow diagram illustrative of an embodiment of a routine toserialize data for communication;

FIG. 45 is a flow diagram illustrative of an embodiment of a routine toexecute a query using a query acceleration data store;

FIG. 46 is a system diagram illustrating an environment for ingestingand indexing data, and performing queries on one or more datasets fromone or more dataset sources including common storage;

FIG. 47 is a flow diagram illustrative of an embodiment of a routine toexecute a query using common storage;

FIG. 48 is a system diagram illustrating an environment for ingestingand indexing data, and performing queries on one or more datasets fromone or more dataset sources including an ingested data buffer;

FIG. 49 is a flow diagram illustrative of an embodiment of a routine toexecute a query using an ingested data buffer;

FIG. 50 is a data flow diagram illustrating an embodiment ofcommunications between different processes within a component of thesystem or between different components of the system to generate adistributed execution model.

FIG. 51 is a flow diagram illustrative of an embodiment of a routineimplemented by the system to generate a distributed execution model.

FIG. 52 is a data flow diagram illustrating an embodiment ofcommunications between different processes within a component of thesystem or between different components of the system to execute adistributed execution model.

FIG. 53 is a flow diagram illustrative of an embodiment of a routineimplemented by the system to execute a distributed execution model.

FIG. 54 is a block diagram illustrating a high-level example of ahardware architecture of a computing system in which an embodiment maybe implemented.

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

1.0. GENERAL OVERVIEW

2.0. OVERVIEW OF DATA INTAKE AND QUERY SYSTEMS

3.0. GENERAL OVERVIEW

-   -   3.1 HOST DEVICES    -   3.2 CLIENT DEVICES    -   3.3. CLIENT DEVICE APPLICATIONS    -   3.4. DATA SERVER SYSTEM    -   3.5. CLOUD-BASED SYSTEM OVERVIEW    -   3.6. SEARCHING EXTERNALLY-ARCHIVED DATA    -   3.7. DATA INGESTION        -   3.7.1. INPUT        -   3.7.2. PARSING        -   3.7.3. INDEXING    -   3.8. QUERY PROCESSING    -   3.9. PIPELINED SEARCH LANGUAGE    -   3.10. FIELD EXTRACTION    -   3.11. EXAMPLE SEARCH SCREEN    -   3.12. DATA MODELS    -   3.13. ACCELERATION TECHNIQUE        -   3.13.1. AGGREGATION TECHNIQUE        -   3.13.2. KEYWORD INDEX        -   3.13.3. HIGH PERFORMANCE ANALYTICS STORE        -   3.13.4. EXTRACTING EVENT DATA USING POSTING        -   3.13.5. ACCELERATING REPORT GENERATION    -   3.14. SECURITY FEATURES    -   3.15. DATA CENTER MONITORING    -   3.16. IT SERVICE MONITORING

4.0. DATA FABRIC SERVICE (DFS)

-   -   4.1. DFS SYSTEM ARCHITECTURE    -   4.2. DFS SYSTEM OPERATIONS

5.0. PARALLEL EXPORT TECHNIQUES

6.0. DFS QUERY PROCESSING

-   -   6.1. ORDERED SEARCH RESULTS    -   6.2. TRANSFORMED SEARCH RESULTS

7.0. CO-LOCATED DEPLOYMENT ARCHITECTURE

-   -   7.1. CO-LOCATED DEPLOYMENT OPERATIONS

8.0. CLOUD DEPLOYMENT ARCHITECTURE

-   -   8.1. CLOUD DEPLOYMENT OPERATIONS

9.0. TIMELINE VISUALIZATION

10.0. MONITORING AND METERING SERVICES

11.0. DATA INTAKE AND FABRIC SYSTEM ARCHITECTURE

-   -   11.1. WORKER NODES        -   11.1.1. SERIALIZATOIN/DESERIALIZATION    -   11.2. SEARCH PROCESS MASTER    -   11.2.1 WORKLOAD CATALOG    -   11.2.2 NODE MONITOR    -   11.2.3 DATASET COMPENSATION    -   11.3. QUERY COORDINATOR        -   11.3.1. QUERY PROCESSING        -   11.3.2. QUERY EXECUTION AND NODE CONTROL        -   11.3.3. RESULT PROCESSING    -   11.4 QUERY ACCELERATION DATA STORE

12.0. QUERY DATA FLOW

13.0. QUERY COORDINATOR FLOW

14.0. QUERY PROCESSING FLOW

15.0. WORKLOAD MONITORING AND ADVISING FLOW

16.0. MULTIPLE DATASET SOURCES FLOW

17.0. EXTERNAL DATA SOURCE FLOW

18.0. DATASET DESTINATION FLOW

19.0. SERIALIZATION AND DESERIALIZATION FLOW

20.0. ACCELERATED QUERY RESULTS FLOW

21.0. COMMON STORAGE ARCHITECTURE

22.0. COMMON STORAGE FLOW

23.0. INGESTED DATA BUFFER ARCHITECTURE

24.0. INGESTED DATA BUFFER FLOW

25.0. HARDWARE EMBODIMENT

26.0. TERMINOLOGY

In this description, references to “an embodiment,” “one embodiment,” orthe like, mean that the particular feature, function, structure orcharacteristic being described is included in at least one embodiment ofthe technique introduced herein. Occurrences of such phrases in thisspecification do not necessarily all refer to the same embodiment. Onthe other hand, the embodiments referred to are also not necessarilymutually exclusive.

A data intake and query system can index and store data in data storesof indexers, and can receive search queries causing a search of theindexers to obtain search results. The data intake and query systemtypically has search, extraction, execution, and analytics capabilitiesthat may be limited in scope to the data stores of the indexers(“internal data stores”). Hence, a seamless and comprehensive search andanalysis that includes diverse data types from external data sources,common storage (may also be referred to as global data storage or globaldata stores), ingested data buffers, query acceleration data stores,etc. may be difficult. Thus, the capabilities of some data intake andquery systems remain isolated from a variety of data sources that couldimprove search results to provide new insights. Furthermore, theprocessing flow of some data intake and query systems are unidirectionalin that data is obtained from a data source, processed, and thencommunicated to a search head or client without the ability to routedata to different destinations.

The disclosed embodiments overcome these drawbacks by extending thesearch and analytics capabilities of a data intake and query system toinclude diverse data types stored in diverse data systems internal to orexternal from the data intake and query system. As a result, an analystcan use the data intake and query system to search and analyze data froma wide variety of dataset sources, including enterprise systems and opensource technologies of a big data ecosystem. The term “big data” refersto large data sets that may be analyzed computationally to revealpatterns, trends, and associations, in some cases, relating to humanbehavior and interactions.

In particular, introduced herein is a data intake and query system thatthat has the ability to execute big data analytics seamlessly and canscale across diverse data sources to enable processing large volumes ofdiverse data from diverse data systems. A “data source” can include a“data system,” which may refer to a system that can process and/or storedata. A “data storage system” may refer to a storage system that canstore data such as unstructured, semi-structured, or structured data.Accordingly, a data source can include a data system that includes adata storage system.

The system can improve search and analytics capabilities of previoussystems by employing a search process master and query coordinatorscombined with a scalable network of distributed nodes communicativelycoupled to diverse data systems. The network of distributed nodes canact as agents of the data intake and query system to collect and processdata of distributed data systems, and the search process master andcoordinators can provide the processed data to the search head as searchresults.

For example, the data intake and query system can respond to a query byexecuting search operations on various internal and external datasources to obtain partial search results that are harmonized andpresented as search results of the query. As such, the data intake andquery system can offload search and analytics operations to thedistributed nodes. Hence, the system enables search and analyticscapabilities that can extend beyond the data stored on indexers toinclude external data systems, common storage, query acceleration datastores, ingested data buffers, etc.

The system can provide big data open stack integration to act as a bigdata pipeline that extends the search and analytics capabilities of asystem over numerous and diverse data sources. For example, the systemcan extend the data execution scope of the data intake and query systemto include data residing in external data systems such as MySQL,PostgreSQL, and Oracle databases; NoSQL data stores like Cassandra,Mongo DB; cloud storage like Amazon S3 and Hadoop distributed filesystem (HDFS); common storage; ingested data buffers; etc. Thus, thesystem can execute search and analytics operations for all possiblecombinations of data types stored in various data sources.

The distributed processing of the system enables scalability to includeany number of distributed data systems. As such, queries received by thedata intake and query system can be propagated to the network ofdistributed nodes to extend the search and analytics capabilities of thedata intake and query system over different data sources. In thiscontext, the network of distributed nodes can act as an extension of thelocal data intake in query system's data processing pipeline tofacilitate scalable analytics across the diverse data systems.Accordingly, the system can extend and transform the data intake andquery system to include data resources into a data fabric platform thatcan leverage computing assets from anywhere and access and execute ondata regardless of type or origin.

The disclosed embodiments include services such as new searchcapabilities, visualization tools, and other services that areseamlessly integrated into the DFS system. For example, the disclosedtechniques include new search services performed on internal datastores, external data stores, or a combination of both. The searchoperations can provide ordered or unordered search results, or searchresults derived from data of diverse data systems, which can bevisualized to provide new and useful insights about the data containedin a big data ecosystem.

Various other features of the DFS system introduced here will becomeapparent from the description that follows. First, however, it is usefulto consider an example of an environment and system in which thetechniques can be employed, as will now be described.

1.0. General Overview

The embodiments disclosed herein generally refer to an environment thatincludes data intake and query system including a data fabric servicesystem architecture (“DFS system”), services, a network of distributednodes, and distributed data systems, all interconnected over one or morenetworks. However, embodiments of the disclosed environment can includemany computing components including software, servers, routers, clientdevices, and host devices that are not specifically described herein. Asused herein, a “node” can refer to one or more devices and/or softwarerunning on devices that enable the devices to provide execute a task ofthe system. For example, a node can include devices running softwarethat enable the device to execute a portion of a query.

FIG. 1A is a high-level system diagram of an environment 10 in which anembodiment may be implemented. The environment 10 includes distributedexternal data systems 12-1 and 12-2 (also referred to collectively andindividually as external data system(s) 12). The external data systems12 are communicatively coupled (e.g., via a LAN, WAN, etc.) to workernodes 14-1 and 14-2 of a data intake and query system 16, respectively(also referred to collectively and individually as worker node(s) 14).The environment 10 can also include a client device 22 and applicationsrunning on the client device 22. An example includes a personalcomputer, laptop, tablet, phone, or other computing device running anetwork browser application that enables a user of the client device 22to access any of the data systems.

The data intake and query system 16 and the external data systems 12 caneach store data obtained from various data sources. For example, thedata intake and query system 16 can store data in internal data stores20 (also referred to as an internal storage system), and the externaldata systems 12 can store data in respective external data stores 22(also referred to as external storage systems). However, the data intakeand query system 16 and external data systems 12 may process and storedata differently. For example, as explained in greater detail below, thedata intake and query system 16 may store minimally processed orunprocessed data (“raw data”) in the internal data stores 20, which canbe implemented as local data stores 20-1, common storage 20-2, or queryacceleration data stores 20-3. In contrast, the external data systems 12may store pre-processed data rather than raw data. Hence, the dataintake and query system 16 and the external data systems 12 can operateindependent of each other in a big data ecosystem.

The worker nodes 14 can act as agents of the data intake and querysystem 16 to process data collected from the internal data stores 20 andthe external data stores 22. The worker nodes 14 may reside on one ormore computing devices such as servers communicatively coupled to theexternal data systems 12. Other components of the data intake and querysystem 16 can finalize the results before returning the results to theclient device 22. As such, the worker nodes 14 can extend the search andanalytics capabilities of the data intake and query system 16 to act ondiverse data systems.

The external data systems 12 may include one or more computing devicesthat can store structured, semi-structured, or unstructured data. Eachexternal data system 12 can generate and/or collect generated data, andstore the generated data in their respective external data stores 22.For example, the external data system 12-1 may include a server runninga MySQL database that stores structured data objects such astime-stamped events, and the external data system 12-2 may be a serverof cloud computing services such as Amazon web services (AWS) that canprovide different data types ranging from unstructured (e.g., s3) tostructured (e.g., redshift).

The internal data stores 20 are said to be internal because the datastored thereon has been processed or passed through the data intake andquery system 16 in some form. Conversely, the external data systems 12are said to be external to the data intake and query system 16 becausethe data stored at the external data stores 22 has not necessarily beenprocessed or passed through the data intake and query system 16. Inother words, the data intake and query system 16 may have no control orinfluence over how data is processed, controlled, or managed by theexternal data systems 12.

The external data systems 12 can process data, perform requests receivedfrom other computing systems, and perform numerous other computationaltasks independent of each other and independent of the data intake andquery system 16. For example, the external data system 12-1 may be aserver that can process data locally that reflects correlations amongthe stored data. The external data systems 12 may generate and/or storeever increasing volumes of data without any interaction with the dataintake and query system 16. As such, each of the external data system 12may act independently to control, manage, and process the data theycontain.

Data stored in the internal data stores 20 and external data stores 22may be related. For example, an online transaction could generatevarious forms of data stored in disparate locations and in variousformats. The generated data may include payment information, customerinformation, and information about suppliers, retailers, and the like.Other examples of data generated in a big data ecosystem includeapplication program data, system logs, network packet data, error logs,stack traces, and performance data. The data can also include diagnosticinformation and many other types of data that can be analyzed to performlocal actions, diagnose performance problems, monitor interactions, andderive other insights.

The volume of generated data can grow at very high rates as the numberof transactions and diverse data systems grows. A portion of this largevolume of data could be processed and stored by the data intake andquery system 16 while other portions could be stored in any of theexternal data systems 12. In an effort to reduce the vast amounts of rawdata generated in a big data ecosystem, some of the external datasystems 12 may pre-process the raw data based on anticipated dataanalysis needs, store the pre-processed data, discard some or all of theremaining raw data, or store it in a different location that data intakeand query system 16 does not have access to. However, discarding or notmaking the massive amounts of raw data available can result in the lossof valuable insights that could have been obtained by searching all ofthe raw data.

In contrast, the data intake and query system 16 can address some ofthese challenges by collecting and storing raw data as structured“events,” as will be described in greater detail below. In someembodiments, an event includes a portion of raw data and is associatedwith a specific point in time. For example, events may be derived from“time series data,” where the time series data comprises a sequence ofdata points (e.g., performance measurements from a computer system) thatare associated with successive points in time.

In some embodiments, the external data systems 12 can store raw data asevents that are indexed by timestamps but are also associated withpredetermined data items. This structure is essentially a modificationof conventional database systems that require predetermining data itemsfor subsequent searches. These systems can be modified to retain theremaining raw data for subsequent re-processing for other predetermineddata items.

Specifically, the raw data can be divided into segments and indexed bytimestamps. The predetermined data items can be associated with theevents indexed by timestamps. The events can be searched only for thepredetermined data items during search time; the events can bere-processed later in time to re-index the raw data, and generate eventswith new predetermined data items. As such, the data systems of thesystem 10 can store related data in a variety of pre-processed data andraw data in a variety of structures.

A number of tools are available to search and analyze data contained inthese diverse data systems. As such, an analyst can use a tool to searcha database of the external data system 12-1. A different tool could beused to search a cloud services application of the external data system12-2. Yet another different tool could be used to search the internaldata stores 20. Moreover, different tools can perform analytics of datastored in proprietary or open source data stores. However, existingtools cannot obtain valuable insights from data contained in acombination of the data intake and query system 16 and/or any of theexternal data systems 12. Examples of these valuable insights mayinclude correlations between the structured data of the external datastores 22 and raw data of the internal data stores 20.

The disclosed techniques can extend the search, extraction, execution,and analytics capabilities of data intake and query systems toseamlessly search and analyze multiple diverse data of diverse datasystems in a big data ecosystem. The disclosed techniques can transforma big data ecosystem into a big data pipeline between external datasystems and a data intake and query system, to enable seamless searchand analytics operations on a variety of data sources, which can lead tonew insights that were not previously available. Hence, the disclosedtechniques include a data intake and query system 16 extended to searchexternal data systems into a data fabric platform that can leveragecomputing assets from anywhere and access and execute on data regardlessof type and origin. In addition, the data intake and query system 16facilitates implementation of both iterative searches, to read datasetsmultiple times in a loop, and interactive or exploratory data analysis(e.g., for repeated database-style querying of data).

2.0. Overview of Data Intake and Query Systems

As indicated above, modern data centers and other computing environmentscan comprise anywhere from a few host computer systems to thousands ofsystems configured to process data, service requests from remoteclients, and perform numerous other computational tasks. Duringoperation, various components within these computing environments oftengenerate significant volumes of machine data. Machine data is any dataproduced by a machine or component in an information technology (IT)environment and that reflects activity in the IT environment. Forexample, machine data can be raw machine data that is generated byvarious components in IT environments, such as servers, sensors,routers, mobile devices, Internet of Things (IoT) devices, etc. Machinedata can include system logs, network packet data, sensor data,application program data, error logs, stack traces, system performancedata, etc. In general, machine data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data. In order toreduce the size of the potentially vast amount of machine data that maybe generated, many of these tools typically pre-process the data basedon anticipated data-analysis needs. For example, pre-specified dataitems may be extracted from the machine data and stored in a database tofacilitate efficient retrieval and analysis of those data items atsearch time. However, the rest of the machine data typically is notsaved and is discarded during pre-processing. As storage capacitybecomes progressively cheaper and more plentiful, there are fewerincentives to discard these portions of machine data and many reasons toretain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and search machine datafrom various websites, applications, servers, networks, and mobiledevices that power their businesses. The data intake and query system isparticularly useful for analyzing data which is commonly found in systemlog files, network data, and other data input sources. Although many ofthe techniques described herein are explained with reference to a dataintake and query system similar to the SPLUNK® ENTERPRISE system, thesetechniques are also applicable to other types of data systems.

In the data intake and query system, machine data are collected andstored as “events”. An event comprises a portion of machine data and isassociated with a specific point in time. The portion of machine datamay reflect activity in an IT environment and may be produced by acomponent of that IT environment, where the events may be searched toprovide insight into the IT environment, thereby improving theperformance of components in the IT environment. Events may be derivedfrom “time series data,” where the time series data comprises a sequenceof data points (e.g., performance measurements from a computer system,etc.) that are associated with successive points in time. In general,each event has a portion of machine data that is associated with atimestamp that is derived from the portion of machine data in the event.A timestamp of an event may be determined through interpolation betweentemporally proximate events having known timestamps or may be determinedbased on other configurable rules for associating timestamps withevents.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data associated withfields in a database table. In other instances, machine data may nothave a predefined format (e.g., may not be at fixed, predefinedlocations), but may have repeatable (e.g., non-random) patterns. Thismeans that some machine data can comprise various data items ofdifferent data types that may be stored at different locations withinthe data. For example, when the data source is an operating system log,an event can include one or more lines from the operating system logcontaining machine data that includes different types of performance anddiagnostic information associated with a specific point in time (e.g., atimestamp).

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The machine data generated bysuch data sources can include, for example and without limitation,server log files, activity log files, configuration files, messages,network packet data, performance measurements, sensor measurements, etc.

The data intake and query system uses a flexible schema to specify howto extract information from events. A flexible schema may be developedand redefined as needed. Note that a flexible schema may be applied toevents “on the fly,” when it is needed (e.g., at search time, indextime, ingestion time, etc.). When the schema is not applied to eventsuntil search time, the schema may be referred to as a “late-bindingschema.”

During operation, the data intake and query system receives machine datafrom any type and number of sources (e.g., one or more system logs,streams of network packet data, sensor data, application program data,error logs, stack traces, system performance data, etc.). The systemparses the machine data to produce events each having a portion ofmachine data associated with a timestamp. The system stores the eventsin a data store. The system enables users to run queries against thestored events to, for example, retrieve events that meet criteriaspecified in a query, such as criteria indicating certain keywords orhaving specific values in defined fields. As used herein, the term“field” refers to a location in the machine data of an event containingone or more values for a specific data item. A field may be referencedby a field name associated with the field. As will be described in moredetail herein, a field is defined by an extraction rule (e.g., a regularexpression) that derives one or more values or a sub-portion of textfrom the portion of machine data in each event to produce a value forthe field for that event. The set of values produced aresemantically-related (such as IP address), even though the machine datain each event may be in different formats (e.g., semantically-relatedvalues may be in different positions in the events derived fromdifferent sources).

As described above, the system stores the events in a data store. Theevents stored in the data store are field-searchable, wherefield-searchable herein refers to the ability to search the machine data(e.g., the raw machine data) of an event based on a field specified insearch criteria. For example, a search having criteria that specifies afield name “UserID” may cause the system to field-search the machinedata of events to identify events that have the field name “UserID.” Inanother example, a search having criteria that specifies a field name“UserID” with a corresponding field value “12345” may cause the systemto field-search the machine data of events to identify events havingthat field-value pair (e.g., field name “UserID” with a correspondingfield value of “12345”). Events are field-searchable using one or moreconfiguration files associated with the events. Each configuration fileincludes one or more field names, where each field name is associatedwith a corresponding extraction rule and a set of events to which thatextraction rule applies. The set of events to which an extraction ruleapplies may be identified by metadata associated with the set of events.For example, an extraction rule may apply to a set of events that areeach associated with a particular host, source, or source type. Whenevents are to be searched based on a particular field name specified ina search, the system uses one or more configuration files to determinewhether there is an extraction rule for that particular field name thatapplies to each event that falls within the criteria of the search. Ifso, the event is considered as part of the search results (andadditional processing may be performed on that event based on criteriaspecified in the search). If not, the next event is similarly analyzed,and so on.

As noted above, the data intake and query system utilizes a late-bindingschema while performing queries on events. One aspect of a late-bindingschema is applying extraction rules to events to extract values forspecific fields during search time. More specifically, the extractionrule for a field can include one or more instructions that specify howto extract a value for the field from an event. An extraction rule cangenerally include any type of instruction for extracting values fromevents. In some cases, an extraction rule comprises a regularexpression, where a sequence of characters form a search pattern. Anextraction rule comprising a regular expression is referred to herein asa regex rule. The system applies a regex rule to an event to extractvalues for a field associated with the regex rule, where the values areextracted by searching the event for the sequence of characters definedin the regex rule.

In the data intake and query system, a field extractor may be configuredto automatically generate extraction rules for certain fields in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields specified in aquery may be provided in the query itself, or may be located duringexecution of the query. Hence, as a user learns more about the data inthe events, the user can continue to refine the late-binding schema byadding new fields, deleting fields, or modifying the field extractionrules for use the next time the schema is used by the system. Becausethe data intake and query system maintains the underlying machine dataand uses a late-binding schema for searching the machine data, itenables a user to continue investigating and learn valuable insightsabout the machine data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent and/or similar data items, even thoughthe fields may be associated with different types of events thatpossibly have different data formats and different extraction rules. Byenabling a common field name to be used to identify equivalent and/orsimilar fields from different types of events generated by disparatedata sources, the system facilitates use of a “common information model”(CIM) across the disparate data sources (further discussed with respectto FIG. 7A).

3.0. General Overview

FIG. 1B is a block diagram of an example networked computer environment100, in accordance with example embodiments. Those skilled in the artwould understand that FIG. 1B represents one example of a networkedcomputer system and other embodiments, such as the embodimentillustrated in FIG. 1A may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In some embodiments, one or more client devices 102 are coupled to oneor more host devices 106 and a data intake and query system 108 via oneor more networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

3.1 Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types of machine data.For example, a host application 114 comprising a web server may generateone or more web server logs in which details of interactions between theweb server and any number of client devices 102 is recorded. As anotherexample, a host device 106 comprising a router may generate one or morerouter logs that record information related to network traffic managedby the router. As yet another example, a host application 114 comprisinga database server may generate one or more logs that record informationrelated to requests sent from other host applications 114 (e.g., webservers or application servers) for data managed by the database server.

3.2 Client Devices

Client devices 102 represent any computing device capable of interactingwith one or more host devices 106 via a network 104. Examples of clientdevices 102 may include, without limitation, smart phones, tabletcomputers, handheld computers, wearable devices, laptop computers,desktop computers, servers, portable media players, gaming devices, andso forth. In general, a client device 102 can provide access todifferent content, for instance, content provided by one or more hostdevices 106, etc. Each client device 102 may comprise one or more clientapplications 110, described in more detail in a separate sectionhereinafter.

3.3. Client Device Applications

In some embodiments, each client device 102 may host or execute one ormore client applications 110 that are capable of interacting with one ormore host devices 106 via one or more networks 104. For instance, aclient application 110 may be or comprise a web browser that a user mayuse to navigate to one or more websites or other resources provided byone or more host devices 106. As another example, a client application110 may comprise a mobile application or “app.” For example, an operatorof a network-based service hosted by one or more host devices 106 maymake available one or more mobile apps that enable users of clientdevices 102 to access various resources of the network-based service. Asyet another example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In some embodiments, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In some embodiments, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some embodiments, an SDK or other code for implementing themonitoring functionality may be offered by a provider of a data intakeand query system, such as a system 108. In such cases, the provider ofthe system 108 can implement the custom code so that performance datagenerated by the monitoring functionality is sent to the system 108 tofacilitate analysis of the performance data by a developer of the clientapplication or other users.

In some embodiments, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In some embodiments, the monitoring component 112 may monitor one ormore aspects of network traffic sent and/or received by a clientapplication 110. For example, the monitoring component 112 may beconfigured to monitor data packets transmitted to and/or from one ormore host applications 114. Incoming and/or outgoing data packets can beread or examined to identify network data contained within the packets,for example, and other aspects of data packets can be analyzed todetermine a number of network performance statistics. Monitoring networktraffic may enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In some embodiments, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In some embodiments, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In some embodiments, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer, and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

3.4. Data Server System

FIG. 2 is a block diagram of an example data intake and query system108, in accordance with example embodiments. System 108 includes one ormore forwarders 204 that receive data from a variety of input datasources 202, and one or more indexers 206 that process and store thedata in one or more data stores 208. These forwarders 204 and indexers206 can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by system 108. Examples of a data sources 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In some embodiments, a forwarder 204 may comprise a service accessibleto client devices 102 and host devices 106 via a network 104. Forexample, one type of forwarder 204 may be capable of consuming vastamounts of real-time data from a potentially large number of clientdevices 102 and/or host devices 106. The forwarder 204 may, for example,comprise a computing device which implements multiple data pipelines or“queues” to handle forwarding of network data to indexers 206. Aforwarder 204 may also perform many of the functions that are performedby an indexer. For example, a forwarder 204 may perform keywordextractions on raw data or parse raw data to create events. A forwarder204 may generate time stamps for events. Additionally or alternatively,a forwarder 204 may perform routing of events to indexers 206. Datastore 208 may contain events derived from machine data from a variety ofsources all pertaining to the same component in an IT environment, andthis data may be produced by the machine in question or by othercomponents in the IT environment.

3.5. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 3 illustrates a block diagram of an example cloud-based data intakeand query system. Similar to the system of FIG. 2 , the networkedcomputer system 300 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system300, one or more forwarders 204 and client devices 302 are coupled to acloud-based data intake and query system 306 via one or more networks304. Network 304 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 302 and forwarders204 to access the system 306. Similar to the system of 38, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 306 forfurther processing.

In some embodiments, a cloud-based data intake and query system 306 maycomprise a plurality of system instances 308. In general, each systeminstance 308 may include one or more computing resources managed by aprovider of the cloud-based system 306 made available to a particularsubscriber. The computing resources comprising a system instance 308may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 302 to access a web portal or otherinterface that enables the subscriber to configure an instance 308.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers, andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 308) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment, such as SPLUNK® ENTERPRISE, and acloud-based environment, such as SPLUNK CLOUD™, are centrally visible).

3.6. Searching Externally-Archived Data

FIG. 4 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the Splunk® Analytics for Hadoop® system provided bySplunk Inc. of San Francisco, Calif. Splunk® Analytics for Hadoop®represents an analytics platform that enables business and IT teams torapidly explore, analyze, and visualize data in Hadoop® and NoSQL datastores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 404 over network connections420. As discussed above, the data intake and query system 108 may residein an enterprise location, in the cloud, etc. FIG. 4 illustrates thatmultiple client devices 404 a, 404 b, . . . , 404 n may communicate withthe data intake and query system 108. The client devices 404 maycommunicate with the data intake and query system using a variety ofconnections. For example, one client device in FIG. 4 is illustrated ascommunicating over an Internet (Web) protocol, another client device isillustrated as communicating via a command line interface, and anotherclient device is illustrated as communicating via a software developerkit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 404 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 410. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 410, 412. FIG. 4 shows two ERP processes 410, 412 that connectto respective remote (external) virtual indices, which are indicated asa Hadoop or another system 414 (e.g., Amazon S3, Amazon EMR, otherHadoop® Compatible File Systems (HCFS), etc.) and a relational databasemanagement system (RDBMS) 416. Other virtual indices may include otherfile organizations and protocols, such as Structured Query Language(SQL) and the like. The ellipses between the ERP processes 410, 412indicate optional additional ERP processes of the data intake and querysystem 108. An ERP process may be a computer process that is initiatedor spawned by the search head 210 and is executed by the search dataintake and query system 108. Alternatively or additionally, an ERPprocess may be a process spawned by the search head 210 on the same ordifferent host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to a SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 410, 412 receive a search request from the search head210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 410, 412 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 410, 412 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 410, 412 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices414, 416, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 404 may communicate with the data intake and query system108 through a network interface 420, e.g., one or more LANs, WANs,cellular networks, intranetworks, and/or internetworks using any ofwired, wireless, terrestrial microwave, satellite links, etc., and mayinclude the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. Pat. No. 9,514,189, entitled “PROCESSING ASYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”, issued on 6 Dec.2016, each of which is hereby incorporated by reference in its entiretyfor all purposes.

3.6.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the machinedata obtained from the external data source) are provided to the searchhead, which can then process the results data (e.g., break the machinedata into events, timestamp it, filter it, etc.) and integrate theresults data with the results data from other external data sources,and/or from data stores of the search head. The search head performssuch processing and can immediately start returning interim (streamingmode) results to the user at the requesting client device;simultaneously, the search head is waiting for the ERP process toprocess the data it is retrieving from the external data source as aresult of the concurrently executing reporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the machined data or unprocesseddata necessary to respond to a search request) to the search head,enabling the search head to process the interim results and beginproviding to the client or search requester interim results that areresponsive to the query. Meanwhile, in this mixed mode, the ERP alsooperates concurrently in reporting mode, processing portions of machinedata in a manner responsive to the search query. Upon determining thatit has results from the reporting mode available to return to the searchhead, the ERP may halt processing in the mixed mode at that time (orsome later time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the machine data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return machine data tothe search head. As noted, the ERP process could be configured tooperate in streaming mode alone and return just the machine data for thesearch head to process in a way that is responsive to the searchrequest. Alternatively, the ERP process can be configured to operate inthe reporting mode only. Also, the ERP process can be configured tooperate in streaming mode and reporting mode concurrently, as described,with the ERP process stopping the transmission of streaming results tothe search head when the concurrently running reporting mode has caughtup and started providing results. The reporting mode does not requirethe processing of all machine data that is responsive to the searchquery request before the ERP process starts returning results; rather,the reporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

3.7. Data Ingestion

FIG. 5A is a flow chart of an example method that illustrates howindexers process, index, and store data received from forwarders, inaccordance with example embodiments. The data flow illustrated in FIG.5A is provided for illustrative purposes only; those skilled in the artwould understand that one or more of the steps of the processesillustrated in FIG. 5A may be removed or that the ordering of the stepsmay be changed. Furthermore, for the purposes of illustrating a clearexample, one or more particular system components are described in thecontext of performing various operations during each of the data flowstages. For example, a forwarder is described as receiving andprocessing machine data during an input phase; an indexer is describedas parsing and indexing machine data during parsing and indexing phases;and a search head is described as performing a search query during asearch phase. However, other system arrangements and distributions ofthe processing steps across system components may be used.

3.7.1. Input

At block 502, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2 . A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In some embodiments, a forwarderreceives the raw data and may segment the data stream into “blocks”,possibly of a uniform data size, to facilitate subsequent processingsteps.

At block 504, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In someembodiments, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The data intake and query system allows forwarding of data from one dataintake and query instance to another, or even to a third-party system.The data intake and query system can employ different types offorwarders in a configuration.

In some embodiments, a forwarder may contain the essential componentsneeded to forward data. A forwarder can gather data from a variety ofinputs and forward the data to an indexer for indexing and searching. Aforwarder can also tag metadata (e.g., source, source type, host, etc.).

In some embodiments, a forwarder has the capabilities of theaforementioned forwarder as well as additional capabilities. Theforwarder can parse data before forwarding the data (e.g., can associatea time stamp with a portion of data and create an event, etc.) and canroute data based on criteria such as source or type of event. Theforwarder can also index data locally while forwarding the data toanother indexer.

3.7.2. Parsing

At block 506, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In some embodiments,to organize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries withinthe received data that indicate the portions of machine data for events.In general, these properties may include regular expression-based rulesor delimiter rules where, for example, event boundaries may be indicatedby predefined characters or character strings. These predefinedcharacters may include punctuation marks or other special charactersincluding, for example, carriage returns, tabs, spaces, line breaks,etc. If a source type for the data is unknown to the indexer, an indexermay infer a source type for the data by examining the structure of thedata. Then, the indexer can apply an inferred source type definition tothe data to create the events.

At block 508, the indexer determines a timestamp for each event. Similarto the process for parsing machine data, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data for the event, tointerpolate time values based on timestamps associated with temporallyproximate events, to create a timestamp based on a time the portion ofmachine data was received or generated, to use the timestamp of aprevious event, or use any other rules for determining timestamps.

At block 510, the indexer associates with each event one or moremetadata fields including a field containing the timestamp determinedfor the event. In some embodiments, a timestamp may be included in themetadata fields. These metadata fields may include any number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 504, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 512, an indexer may optionally apply one or moretransformations to data included in the events created at block 506. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to events may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

FIG. 5C illustrates an illustrative example of machine data can bestored in a data store in accordance with various disclosed embodiments.In other embodiments, machine data can be stored in a flat file in acorresponding bucket with an associated index file, such as a timeseries index or “TSIDX.” As such, the depiction of machine data andassociated metadata as rows and columns in the table of FIG. 5C ismerely illustrative and is not intended to limit the data format inwhich the machine data and metadata is stored in various embodimentsdescribed herein. In one particular embodiment, machine data can bestored in a compressed or encrypted formatted. In such embodiments, themachine data can be stored with or be associated with data thatdescribes the compression or encryption scheme with which the machinedata is stored. The information about the compression or encryptionscheme can be used to decompress or decrypt the machine data, and anymetadata with which it is stored, at search time.

As mentioned above, certain metadata, e.g., host 536, source 537, sourcetype 538, and timestamps 535 can be generated for each event, andassociated with a corresponding portion of machine data 539 when storingthe event data in a data store, e.g., data store 208. Any of themetadata can be extracted from the corresponding machine data, orsupplied or defined by an entity, such as a user or computer system. Themetadata fields can become part of or stored with the event. Note thatwhile the time-stamp metadata field can be extracted from the raw dataof each event, the values for the other metadata fields may bedetermined by the indexer based on information it receives pertaining tothe source of the data separate from the machine data.

While certain default or user-defined metadata fields can be extractedfrom the machine data for indexing purposes, all the machine data withinan event can be maintained in its original condition. As such, inembodiments in which the portion of machine data included in an event isunprocessed or otherwise unaltered, it is referred to herein as aportion of raw machine data. In other embodiments, the port of machinedata in an event can be processed or otherwise altered. As such, unlesscertain information needs to be removed for some reasons (e.g.extraneous information, confidential information), all the raw machinedata contained in an event can be preserved and saved in its originalform. Accordingly, the data store in which the event records are storedis sometimes referred to as a “raw record data store.” The raw recorddata store contains a record of the raw event data tagged with thevarious default fields.

In FIG. 5C, the first three rows of the table represent events 531, 532,and 533 and are related to a server access log that records requestsfrom multiple clients processed by a server, as indicated by entry of“access.log” in the source column 536.

In the example shown in FIG. 5C, each of the events 531-534 isassociated with a discrete request made from a client device. The rawmachine data generated by the server and extracted from a server accesslog can include the IP address of the client 540, the user id of theperson requesting the document 541, the time the server finishedprocessing the request 542, the request line from the client 543, thestatus code returned by the server to the client 545, the size of theobject returned to the client (in this case, the gif file requested bythe client) 546 and the time spent to serve the request in microseconds544. As seen in FIG. 5C, all the raw machine data retrieved from theserver access log is retained and stored as part of the correspondingevents, 1221, 1222, and 1223 in the data store.

Event 534 is associated with an entry in a server error log, asindicated by “error.log” in the source column 537 that records errorsthat the server encountered when processing a client request. Similar tothe events related to the server access log, all the raw machine data inthe error log file pertaining to event 534 can be preserved and storedas part of the event 534.

Saving minimally processed or unprocessed machine data in a data storeassociated with metadata fields in the manner similar to that shown inFIG. 5C is advantageous because it allows search of all the machine dataat search time instead of searching only previously specified andidentified fields or field-value pairs. As mentioned above, because datastructures used by various embodiments of the present disclosuremaintain the underlying raw machine data and use a late-binding schemafor searching the raw machines data, it enables a user to continueinvestigating and learn valuable insights about the raw data. In otherwords, the user is not compelled to know about all the fields ofinformation that will be needed at data ingestion time. As a user learnsmore about the data in the events, the user can continue to refine thelate-binding schema by defining new extraction rules, or modifying ordeleting existing extraction rules used by the system.

3.7.3. Indexing

At blocks 514 and 516, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for events. To build akeyword index, at block 514, the indexer identifies a set of keywords ineach event. At block 516, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries for fieldname-value pairs found in events, where a field name-value pair caninclude a pair of keywords connected by a symbol, such as an equals signor colon. This way, events containing these field name-value pairs canbe quickly located. In some embodiments, fields can automatically begenerated for some or all of the field names of the field name-valuepairs at the time of indexing. For example, if the string“dest=10.0.1.2” is found in an event, a field named “dest” may becreated for the event, and assigned a value of “10.0.1.2”.

At block 518, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In some embodiments, the stored events are organizedinto “buckets,” where each bucket stores events associated with aspecific time range based on the timestamps associated with each event.This improves time-based searching, as well as allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk. In some embodiments, eachbucket may be associated with an identifier, a time range, and a sizeconstraint.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize the data retrieval process by searchingbuckets corresponding to time ranges that are relevant to a query.

In some embodiments, each indexer has a home directory and a colddirectory. The home directory of an indexer stores hot buckets and warmbuckets, and the cold directory of an indexer stores cold buckets. A hotbucket is a bucket that is capable of receiving and storing events. Awarm bucket is a bucket that can no longer receive events for storagebut has not yet been moved to the cold directory. A cold bucket is abucket that can no longer receive events and may be a bucket that waspreviously stored in the home directory. The home directory may bestored in faster memory, such as flash memory, as events may be activelywritten to the home directory, and the home directory may typicallystore events that are more frequently searched and thus are accessedmore frequently. The cold directory may be stored in slower and/orlarger memory, such as a hard disk, as events are no longer beingwritten to the cold directory, and the cold directory may typicallystore events that are not as frequently searched and thus are accessedless frequently. In some embodiments, an indexer may also have aquarantine bucket that contains events having potentially inaccurateinformation, such as an incorrect time stamp associated with the eventor a time stamp that appears to be an unreasonable time stamp for thecorresponding event. The quarantine bucket may have events from any timerange; as such, the quarantine bucket may always be searched at searchtime. Additionally, an indexer may store old, archived data in a frozenbucket that is not capable of being searched at search time. In someembodiments, a frozen bucket may be stored in slower and/or largermemory, such as a hard disk, and may be stored in offline and/or remotestorage.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. Pat. No. 9,130,971, entitled “SITE-BASEDSEARCH AFFINITY”, issued on 8 Sep. 2015, and in U.S. patent Ser. No.14/266,817, entitled “MULTI-SITE CLUSTERING”, issued on 1 Sep. 2015,each of which is hereby incorporated by reference in its entirety forall purposes.

As will be described in greater detail below with reference to, interalia, FIGS. 18-49 , some functionality of the indexer can be handled bydifferent components of the system. For example, in some cases, theindexer indexes semi-processed, or cooked data (e.g., data that has beenparsed and/or had some fields determined for it), and stores the resultsin common storage.

FIG. 5B is a block diagram of an example data store 501 that includes adirectory for each index (or partition) that contains a portion of datamanaged by an indexer. FIG. 5B further illustrates details of anembodiment of an inverted index 507B and an event reference array 515associated with inverted index 507B.

The data store 501 can correspond to a data store 208 that stores eventsmanaged by an indexer 206 or can correspond to a different data storeassociated with an indexer 206. In the illustrated embodiment, the datastore 501 includes a _main directory 503 associated with a _main indexand a _test directory 505 associated with a _test index. However, thedata store 501 can include fewer or more directories. In someembodiments, multiple indexes can share a single directory or allindexes can share a common directory. Additionally, although illustratedas a single data store 501, it will be understood that the data store501 can be implemented as multiple data stores storing differentportions of the information shown in FIG. 5B. For example, a singleindex or partition can span multiple directories or multiple datastores, and can be indexed or searched by multiple correspondingindexers.

In the illustrated embodiment of FIG. 5B, the index-specific directories503 and 505 include inverted indexes 507A, 507B and 509A, 509B,respectively. The inverted indexes 507A . . . 507B, and 509A . . . 509Bcan be keyword indexes or field-value pair indexes described herein andcan include less or more information that depicted in FIG. 5B.

In some embodiments, each inverted index 507A . . . 507B, and 509A . . .509B can correspond to a distinct time-series bucket that is managed bythe indexer 206 and that contains events corresponding to the relevantindex (e.g., _main index, _test index). As such, each inverted index cancorrespond to a particular range of time for an index. Additional files,such as high performance indexes for each time-series bucket of anindex, can also be stored in the same directory as the inverted indexes507A . . . 507B, and 509A . . . 509B. In some embodiments inverted index507A . . . 507B, and 509A . . . 509B can correspond to multipletime-series buckets or inverted indexes 507A . . . 507B, and 509A . . .509B can correspond to a single time-series bucket.

Each inverted index 507A . . . 507B, and 509A . . . 509B can include oneor more entries, such as keyword (or token) entries or field-value pairentries. Furthermore, in certain embodiments, the inverted indexes 507A. . . 507B, and 509A . . . 509B can include additional information, suchas a time range 523 associated with the inverted index or an indexidentifier 525 identifying the index associated with the inverted index507A . . . 507B, and 509A . . . 509B. However, each inverted index 507A. . . 507B, and 509A . . . 509B can include less or more informationthan depicted.

Token entries, such as token entries 511 illustrated in inverted index507B, can include a token 511A (e.g., “error,” “itemID,” etc.) and eventreferences 511B indicative of events that include the token. Forexample, for the token “error,” the corresponding token entry includesthe token “error” and an event reference, or unique identifier, for eachevent stored in the corresponding time-series bucket that includes thetoken “error.” In the illustrated embodiment of FIG. 5B, the error tokenentry includes the identifiers 3, 5, 6, 8, 11, and 12 corresponding toevents managed by the indexer 206 and associated with the index _main503 that are located in the time-series bucket associated with theinverted index 507B.

In some cases, some token entries can be default entries, automaticallydetermined entries, or user specified entries. In some embodiments, theindexer 206 can identify each word or string in an event as a distincttoken and generate a token entry for it. In some cases, the indexer 206can identify the beginning and ending of tokens based on punctuation,spaces, as described in greater detail herein. In certain cases, theindexer 206 can rely on user input or a configuration file to identifytokens for token entries 511, etc. It will be understood that anycombination of token entries can be included as a default, automaticallydetermined, a or included based on user-specified criteria.

Similarly, field-value pair entries, such as field-value pair entries513 shown in inverted index 507B, can include a field-value pair 513Aand event references 513B indicative of events that include a fieldvalue that corresponds to the field-value pair. For example, for afield-value pair sourcetype::sendmail, a field-value pair entry wouldinclude the field-value pair sourcetype::sendmail and a uniqueidentifier, or event reference, for each event stored in thecorresponding time-series bucket that includes a sendmail sourcetype.

In some cases, the field-value pair entries 513 can be default entries,automatically determined entries, or user specified entries. As anon-limiting example, the field-value pair entries for the fields host,source, sourcetype can be included in the inverted indexes 507A . . .507B, and 509A . . . 509B as a default. As such, all of the invertedindexes 507A . . . 507B, and 509A . . . 509B can include field-valuepair entries for the fields host, source, sourcetype. As yet anothernon-limiting example, the field-value pair entries for the IP_addressfield can be user specified and may only appear in the inverted index507B based on user-specified criteria. As another non-limiting example,as the indexer indexes the events, it can automatically identifyfield-value pairs and create field-value pair entries. For example,based on the indexers review of events, it can identify IP_address as afield in each event and add the IP_address field-value pair entries tothe inverted index 507B. It will be understood that any combination offield-value pair entries can be included as a default, automaticallydetermined, or included based on user-specified criteria.

Each unique identifier 517, or event reference, can correspond to aunique event located in the time series bucket. However, the same eventreference can be located in multiple entries. For example if an eventhas a sourcetype splunkd, host www1 and token “warning,” then the uniqueidentifier for the event will appear in the field-value pair entriessourcetype::splunkd and host::www1, as well as the token entry“warning.” With reference to the illustrated embodiment of FIG. 5B andthe event that corresponds to the event reference 3, the event reference3 is found in the field-value pair entries 513 host::hostA,source::sourceB, sourcetype::sourcetypeA, and IP_address::91.205.189.15indicating that the event corresponding to the event reference 3 is fromhostA, sourceB, of sourcetypeA, and includes 91.205.189.15 in the eventdata.

For some fields, the unique identifier is located in only onefield-value pair entry for a particular field. For example, the invertedindex may include four sourcetype field-value pair entries correspondingto four different sourcetypes of the events stored in a bucket (e.g.,sourcetypes: sendmail, splunkd, web_access, and web_service). Withinthose four sourcetype field-value pair entries, an identifier for aparticular event may appear in only one of the field-value pair entries.With continued reference to the example illustrated embodiment of FIG.5B, since the event reference 7 appears in the field-value pair entrysourcetype::sourcetypeA, then it does not appear in the otherfield-value pair entries for the sourcetype field, includingsourcetype::sourcetypeB, sourcetype::sourcetypeC, andsourcetype::sourcetypeD.

The event references 517 can be used to locate the events in thecorresponding bucket. For example, the inverted index can include, or beassociated with, an event reference array 515. The event reference array515 can include an array entry 517 for each event reference in theinverted index 507B. Each array entry 517 can include locationinformation 519 of the event corresponding to the unique identifier(non-limiting example: seek address of the event), a timestamp 521associated with the event, or additional information regarding the eventassociated with the event reference, etc.

For each token entry 511 or field-value pair entry 513, the eventreference 501B or unique identifiers can be listed in chronologicalorder or the value of the event reference can be assigned based onchronological data, such as a timestamp associated with the eventreferenced by the event reference. For example, the event reference 1 inthe illustrated embodiment of FIG. 5B can correspond to thefirst-in-time event for the bucket, and the event reference 12 cancorrespond to the last-in-time event for the bucket. However, the eventreferences can be listed in any order, such as reverse chronologicalorder, ascending order, descending order, or some other order, etc.Further, the entries can be sorted. For example, the entries can besorted alphabetically (collectively or within a particular group), byentry origin (e.g., default, automatically generated, user-specified,etc.), by entry type (e.g., field-value pair entry, token entry, etc.),or chronologically by when added to the inverted index, etc. In theillustrated embodiment of FIG. 5B, the entries are sorted first by entrytype and then alphabetically.

As a non-limiting example of how the inverted indexes 507A . . . 507B,and 509A . . . 509B can be used during a data categorization requestcommand, the indexers can receive filter criteria indicating data thatis to be categorized and categorization criteria indicating how the datais to be categorized. Example filter criteria can include, but is notlimited to, indexes (or partitions), hosts, sources, sourcetypes, timeranges, field identifier, keywords, etc.

Using the filter criteria, the indexer identifies relevant invertedindexes to be searched. For example, if the filter criteria includes aset of partitions, the indexer can identify the inverted indexes storedin the directory corresponding to the particular partition as relevantinverted indexes. Other means can be used to identify inverted indexesassociated with a partition of interest. For example, in someembodiments, the indexer can review an entry in the inverted indexes,such as an index-value pair entry 513 to determine if a particularinverted index is relevant. If the filter criteria does not identify anypartition, then the indexer can identify all inverted indexes managed bythe indexer as relevant inverted indexes.

Similarly, if the filter criteria includes a time range, the indexer canidentify inverted indexes corresponding to buckets that satisfy at leasta portion of the time range as relevant inverted indexes. For example,if the time range is last hour then the indexer can identify allinverted indexes that correspond to buckets storing events associatedwith timestamps within the last hour as relevant inverted indexes.

When used in combination, an index filter criterion specifying one ormore partitions and a time range filter criterion specifying aparticular time range can be used to identify a subset of invertedindexes within a particular directory (or otherwise associated with aparticular partition) as relevant inverted indexes. As such, the indexercan focus the processing to only a subset of the total number ofinverted indexes that the indexer manages.

Once the relevant inverted indexes are identified, the indexer canreview them using any additional filter criteria to identify events thatsatisfy the filter criteria. In some cases, using the known location ofthe directory in which the relevant inverted indexes are located, theindexer can determine that any events identified using the relevantinverted indexes satisfy an index filter criterion. For example, if thefilter criteria includes a partition main, then the indexer candetermine that any events identified using inverted indexes within thepartition main directory (or otherwise associated with the partitionmain) satisfy the index filter criterion.

Furthermore, based on the time range associated with each invertedindex, the indexer can determine that that any events identified using aparticular inverted index satisfies a time range filter criterion. Forexample, if a time range filter criterion is for the last hour and aparticular inverted index corresponds to events within a time range of50 minutes ago to 35 minutes ago, the indexer can determine that anyevents identified using the particular inverted index satisfy the timerange filter criterion. Conversely, if the particular inverted indexcorresponds to events within a time range of 59 minutes ago to 62minutes ago, the indexer can determine that some events identified usingthe particular inverted index may not satisfy the time range filtercriterion.

Using the inverted indexes, the indexer can identify event references(and therefore events) that satisfy the filter criteria. For example, ifthe token “error” is a filter criterion, the indexer can track all eventreferences within the token entry “error.” Similarly, the indexer canidentify other event references located in other token entries orfield-value pair entries that match the filter criteria. The system canidentify event references located in all of the entries identified bythe filter criteria. For example, if the filter criteria include thetoken “error” and field-value pair sourcetype::web_ui, the indexer cantrack the event references found in both the token entry “error” and thefield-value pair entry sourcetype::web_ui. As mentioned previously, insome cases, such as when multiple values are identified for a particularfilter criterion (e.g., multiple sources for a source filter criterion),the system can identify event references located in at least one of theentries corresponding to the multiple values and in all other entriesidentified by the filter criteria. The indexer can determine that theevents associated with the identified event references satisfy thefilter criteria.

In some cases, the indexer can further consult a timestamp associatedwith the event reference to determine whether an event satisfies thefilter criteria. For example, if an inverted index corresponds to a timerange that is partially outside of a time range filter criterion, thenthe indexer can consult a timestamp associated with the event referenceto determine whether the corresponding event satisfies the time rangecriterion. In some embodiments, to identify events that satisfy a timerange, the indexer can review an array, such as the event referencearray 1614 that identifies the time associated with the events.Furthermore, as mentioned above using the known location of thedirectory in which the relevant inverted indexes are located (or otherindex identifier), the indexer can determine that any events identifiedusing the relevant inverted indexes satisfy the index filter criterion.

In some cases, based on the filter criteria, the indexer reviews anextraction rule. In certain embodiments, if the filter criteria includesa field name that does not correspond to a field-value pair entry in aninverted index, the indexer can review an extraction rule, which may belocated in a configuration file, to identify a field that corresponds toa field-value pair entry in the inverted index.

For example, the filter criteria includes a field name “sessionID” andthe indexer determines that at least one relevant inverted index doesnot include a field-value pair entry corresponding to the field namesessionID, the indexer can review an extraction rule that identifies howthe sessionID field is to be extracted from a particular host, source,or sourcetype (implicitly identifying the particular host, source, orsourcetype that includes a sessionID field). The indexer can replace thefield name “sessionID” in the filter criteria with the identified host,source, or sourcetype. In some cases, the field name “sessionID” may beassociated with multiples hosts, sources, or sourcetypes, in which case,all identified hosts, sources, and sourcetypes can be added as filtercriteria. In some cases, the identified host, source, or sourcetype canreplace or be appended to a filter criterion, or be excluded. Forexample, if the filter criteria includes a criterion for source S1 andthe “sessionID” field is found in source S2, the source S2 can replaceS1 in the filter criteria, be appended such that the filter criteriaincludes source S1 and source S2, or be excluded based on the presenceof the filter criterion source S1. If the identified host, source, orsourcetype is included in the filter criteria, the indexer can thenidentify a field-value pair entry in the inverted index that includes afield value corresponding to the identity of the particular host,source, or sourcetype identified using the extraction rule.

Once the events that satisfy the filter criteria are identified, thesystem, such as the indexer 206 can categorize the results based on thecategorization criteria. The categorization criteria can includecategories for grouping the results, such as any combination ofpartition, source, sourcetype, or host, or other categories or fields asdesired.

The indexer can use the categorization criteria to identifycategorization criteria-value pairs or categorization criteria values bywhich to categorize or group the results. The categorizationcriteria-value pairs can correspond to one or more field-value pairentries stored in a relevant inverted index, one or more index-valuepairs based on a directory in which the inverted index is located or anentry in the inverted index (or other means by which an inverted indexcan be associated with a partition), or other criteria-value pair thatidentifies a general category and a particular value for that category.The categorization criteria values can correspond to the value portionof the categorization criteria-value pair.

As mentioned, in some cases, the categorization criteria-value pairs cancorrespond to one or more field-value pair entries stored in therelevant inverted indexes. For example, the categorizationcriteria-value pairs can correspond to field-value pair entries of host,source, and sourcetype (or other field-value pair entry as desired). Forinstance, if there are ten different hosts, four different sources, andfive different sourcetypes for an inverted index, then the invertedindex can include ten host field-value pair entries, four sourcefield-value pair entries, and five sourcetype field-value pair entries.The indexer can use the nineteen distinct field-value pair entries ascategorization criteria-value pairs to group the results.

Specifically, the indexer can identify the location of the eventreferences associated with the events that satisfy the filter criteriawithin the field-value pairs, and group the event references based ontheir location. As such, the indexer can identify the particular fieldvalue associated with the event corresponding to the event reference.For example, if the categorization criteria include host and sourcetype,the host field-value pair entries and sourcetype field-value pairentries can be used as categorization criteria-value pairs to identifythe specific host and sourcetype associated with the events that satisfythe filter criteria.

In addition, as mentioned, categorization criteria-value pairs cancorrespond to data other than the field-value pair entries in therelevant inverted indexes. For example, if partition or index is used asa categorization criterion, the inverted indexes may not includepartition field-value pair entries. Rather, the indexer can identify thecategorization criteria-value pair associated with the partition basedon the directory in which an inverted index is located, information inthe inverted index, or other information that associates the invertedindex with the partition, etc. As such a variety of methods can be usedto identify the categorization criteria-value pairs from thecategorization criteria.

Accordingly based on the categorization criteria (and categorizationcriteria-value pairs), the indexer can generate groupings based on theevents that satisfy the filter criteria. As a non-limiting example, ifthe categorization criteria includes a partition and sourcetype, thenthe groupings can correspond to events that are associated with eachunique combination of partition and sourcetype. For instance, if thereare three different partitions and two different sourcetypes associatedwith the identified events, then the six different groups can be formed,each with a unique partition value-sourcetype value combination.Similarly, if the categorization criteria includes partition,sourcetype, and host and there are two different partitions, threesourcetypes, and five hosts associated with the identified events, thenthe indexer can generate up to thirty groups for the results thatsatisfy the filter criteria. Each group can be associated with a uniquecombination of categorization criteria-value pairs (e.g., uniquecombinations of partition value sourcetype value, and host value).

In addition, the indexer can count the number of events associated witheach group based on the number of events that meet the uniquecombination of categorization criteria for a particular group (or matchthe categorization criteria-value pairs for the particular group). Withcontinued reference to the example above, the indexer can count thenumber of events that meet the unique combination of partition,sourcetype, and host for a particular group.

Each indexer communicates the groupings to the search head. The searchhead can aggregate the groupings from the indexers and provide thegroupings for display. In some cases, the groups are displayed based onat least one of the host, source, sourcetype, or partition associatedwith the groupings. In some embodiments, the search head can furtherdisplay the groups based on display criteria, such as a display order ora sort order as described in greater detail above.

As a non-limiting example and with reference to FIG. 5B, consider arequest received by an indexer 206 that includes the following filtercriteria: keyword=error, partition=_main, time range=3/1/1716:22.00.000-16:28.00.000, sourcetype=sourcetypeC, host=hostB, and thefollowing categorization criteria: source.

Based on the above criteria, the indexer 206 identifies _main directory503 and can ignore _test directory 505 and any other partition-specificdirectories. The indexer determines that inverted partition 507B is arelevant partition based on its location within the _main directory 503and the time range associated with it. For sake of simplicity in thisexample, the indexer 206 determines that no other inverted indexes inthe _main directory 503, such as inverted index 507A satisfy the timerange criterion.

Having identified the relevant inverted index 507B, the indexer reviewsthe token entries 511 and the field-value pair entries 513 to identifyevent references, or events, that satisfy all of the filter criteria.

With respect to the token entries 511, the indexer can review the errortoken entry and identify event references 3, 5, 6, 8, 11, 12, indicatingthat the term “error” is found in the corresponding events. Similarly,the indexer can identify event references 4, 5, 6, 8, 9, 10, 11 in thefield-value pair entry sourcetype::sourcetypeC and event references 2,5, 6, 8, 10, 11 in the field-value pair entry host::hostB. As the filtercriteria did not include a source or an IP_address field-value pair, theindexer can ignore those field-value pair entries.

In addition to identifying event references found in at least one tokenentry or field-value pair entry (e.g., event references 3, 4, 5, 6, 8,9, 10, 11, 12), the indexer can identify events (and corresponding eventreferences) that satisfy the time range criterion using the eventreference array 1614 (e.g., event references 2, 3, 4, 5, 6, 7, 8, 9,10). Using the information obtained from the inverted index 507B(including the event reference array 515), the indexer 206 can identifythe event references that satisfy all of the filter criteria (e.g.,event references 5, 6, 8).

Having identified the events (and event references) that satisfy all ofthe filter criteria, the indexer 206 can group the event referencesusing the received categorization criteria (source). In doing so, theindexer can determine that event references 5 and 6 are located in thefield-value pair entry source::sourceD (or have matching categorizationcriteria-value pairs) and event reference 8 is located in thefield-value pair entry source::sourceC. Accordingly, the indexer cangenerate a sourceC group having a count of one corresponding toreference 8 and a sourceD group having a count of two corresponding toreferences 5 and 6. This information can be communicated to the searchhead. In turn the search head can aggregate the results from the variousindexers and display the groupings. As mentioned above, in someembodiments, the groupings can be displayed based at least in part onthe categorization criteria, including at least one of host, source,sourcetype, or partition.

It will be understood that a change to any of the filter criteria orcategorization criteria can result in different groupings. As a onenon-limiting example, a request received by an indexer 206 that includesthe following filter criteria: partition=_main, time range=3/1/17 3/1/1716:21:20.000-16:28:17.000, and the following categorization criteria:host, source, sourcetype would result in the indexer identifying eventreferences 1-12 as satisfying the filter criteria. The indexer wouldthen generate up to 24 groupings corresponding to the 24 differentcombinations of the categorization criteria-value pairs, including host(hostA, hostB), source (sourceA, sourceB, sourceC, sourceD), andsourcetype (sourcetypeA, sourcetypeB, sourcetypeC). However, as thereare only twelve events identifiers in the illustrated embodiment andsome fall into the same grouping, the indexer generates eight groups andcounts as follows:

Group 1 (hostA, sourceA, sourcetypeA): 1 (event reference 7)

Group 2 (hostA, sourceA, sourcetypeB): 2 (event references 1, 12)

Group 3 (hostA, sourceA, sourcetypeC): 1 (event reference 4)

Group 4 (hostA, sourceB, sourcetypeA): 1 (event reference 3)

Group 5 (hostA, sourceB, sourcetypeC): 1 (event reference 9)

Group 6 (hostB, sourceC, sourcetypeA): 1 (event reference 2)

Group 7 (hostB, sourceC, sourcetypeC): 2 (event references 8, 11)

Group 8 (hostB, sourceD, sourcetypeC): 3 (event references 5, 6, 10)

As noted, each group has a unique combination of categorizationcriteria-value pairs or categorization criteria values. The indexercommunicates the groups to the search head for aggregation with resultsreceived from other indexers. In communicating the groups to the searchhead, the indexer can include the categorization criteria-value pairsfor each group and the count. In some embodiments, the indexer caninclude more or less information. For example, the indexer can includethe event references associated with each group and other identifyinginformation, such as the indexer or inverted index used to identify thegroups.

As another non-limiting examples, a request received by an indexer 206that includes the following filter criteria: partition=_main, timerange=3/1/17 3/1/17 16:21:20.000-16:28:17.000, source=sourceA, sourceD,and keyword=itemID and the following categorization criteria: host,source, sourcetype would result in the indexer identifying eventreferences 4, 7, and 10 as satisfying the filter criteria, and generatethe following groups:

Group 1 (hostA, sourceA, sourcetypeC): 1 (event reference 4)

Group 2 (hostA, sourceA, sourcetypeA): 1 (event reference 7)

Group 3 (hostB, sourceD, sourcetypeC): 1 (event references 10)

The indexer communicates the groups to the search head for aggregationwith results received from other indexers. As will be understand thereare myriad ways for filtering and categorizing the events and eventreferences. For example, the indexer can review multiple invertedindexes associated with an partition or review the inverted indexes ofmultiple partitions, and categorize the data using any one or anycombination of partition, host, source, sourcetype, or other category,as desired.

Further, if a user interacts with a particular group, the indexer canprovide additional information regarding the group. For example, theindexer can perform a targeted search or sampling of the events thatsatisfy the filter criteria and the categorization criteria for theselected group, also referred to as the filter criteria corresponding tothe group or filter criteria associated with the group.

In some cases, to provide the additional information, the indexer relieson the inverted index. For example, the indexer can identify the eventreferences associated with the events that satisfy the filter criteriaand the categorization criteria for the selected group and then use theevent reference array 515 to access some or all of the identifiedevents. In some cases, the categorization criteria values orcategorization criteria-value pairs associated with the group becomepart of the filter criteria for the review.

With reference to FIG. 5B for instance, suppose a group is displayedwith a count of six corresponding to event references 4, 5, 6, 8, 10, 11(i.e., event references 4, 5, 6, 8, 10, 11 satisfy the filter criteriaand are associated with matching categorization criteria values orcategorization criteria-value pairs) and a user interacts with the group(e.g., selecting the group, clicking on the group, etc.). In response,the search head communicates with the indexer to provide additionalinformation regarding the group.

In some embodiments, the indexer identifies the event referencesassociated with the group using the filter criteria and thecategorization criteria for the group (e.g., categorization criteriavalues or categorization criteria-value pairs unique to the group).Together, the filter criteria and the categorization criteria for thegroup can be referred to as the filter criteria associated with thegroup. Using the filter criteria associated with the group, the indexeridentifies event references 4, 5, 6, 8, 10, 11.

Based on a sampling criteria, discussed in greater detail above, theindexer can determine that it will analyze a sample of the eventsassociated with the event references 4, 5, 6, 8, 10, 11. For example,the sample can include analyzing event data associated with the eventreferences 5, 8, 10. In some embodiments, the indexer can use the eventreference array 1616 to access the event data associated with the eventreferences 5, 8, 10. Once accessed, the indexer can compile the relevantinformation and provide it to the search head for aggregation withresults from other indexers. By identifying events and sampling eventdata using the inverted indexes, the indexer can reduce the amount ofactual data this is analyzed and the number of events that are accessedin order to generate the summary of the group and provide a response inless time.

3.8. Query Processing

FIG. 6A is a flow diagram of an example method that illustrates how asearch head and indexers perform a search query, in accordance withexample embodiments. At block 602, a search head receives a search queryfrom a client. At block 604, the search head analyzes the search queryto determine what portion(s) of the query can be delegated to indexersand what portions of the query can be executed locally by the searchhead. At block 606, the search head distributes the determined portionsof the query to the appropriate indexers. In some embodiments, a searchhead cluster may take the place of an independent search head where eachsearch head in the search head cluster coordinates with peer searchheads in the search head cluster to schedule jobs, replicate searchresults, update configurations, fulfill search requests, etc. In someembodiments, the search head (or each search head) communicates with amaster node (also known as a cluster master, not shown in FIG. 2 ) thatprovides the search head with a list of indexers to which the searchhead can distribute the determined portions of the query. The masternode maintains a list of active indexers and can also designate whichindexers may have responsibility for responding to queries over certainsets of events. A search head may communicate with the master nodebefore the search head distributes queries to indexers to discover theaddresses of active indexers.

At block 608, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 608 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In some embodiments, one or morerules for extracting field values may be specified as part of a sourcetype definition in a configuration file. The indexers may then eithersend the relevant events back to the search head, or use the events todetermine a partial result, and send the partial result back to thesearch head.

At block 610, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Insome examples, the results of the query are indicative of performance orsecurity of the IT environment and may help improve the performance ofcomponents in the IT environment. This final result may comprisedifferent types of data depending on what the query requested. Forexample, the results can include a listing of matching events returnedby the query, or some type of visualization of the data from thereturned events. In another example, the final result can include one ormore calculated values derived from the matching events.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries, which may beparticularly helpful for queries that are performed on a periodic basis.

As will be described in greater detail below with reference to, interalia, FIGS. 18-49 , some functionality of the search head or indexerscan be handled by different components of the system or removedaltogether. For example, in some cases, a query coordinator analyzes thequery, identifies dataset sources to be accessed, generates subqueriesfor execution by dataset sources, such as indexers, collects partialresults to produce a final result and returns the final results to thesearch head for delivery to a client device or delivers the finalresults to the client device without the search head. In some cases,results from dataset sources, such as the indexers, are communicated tonodes, which further process the data, and communicate the results ofthe processing to the query coordinator, etc. In some embodiments, thesearch head spawns a search process, which communicates the query to asearch process master. The search process master can communicate thequery to the query coordinator for processing and execution.

In addition, in some embodiments, the indexers are not involved insearch operations or only search some data, such as data in hot buckets,etc. For example, nodes can perform the search functionality describedherein with respect to indexers. For example, nodes can use late-bindingschema to extract values for specified fields from events at the timethe query is processed and/or use one or more rules specified as part ofa source type definition in a configuration file for extracting fieldvalues, etc. Furthermore, in some embodiments, nodes can perform searchoperations on data in common storage or found in other dataset sources,such as external data stores, query acceleration data stores, ingesteddata buffers, etc.

3.9. Pipelined Search Language

Various embodiments of the present disclosure can be implemented using,or in conjunction with, a pipelined command language. A pipelinedcommand language is a language in which a set of inputs or data isoperated on by a first command in a sequence of commands, and thensubsequent commands in the order they are arranged in the sequence. Suchcommands can include any type of functionality for operating on data,such as retrieving, searching, filtering, aggregating, processing,transmitting, and the like. As described herein, a query can thus beformulated in a pipelined command language and include any number ofordered or unordered commands for operating on data.

Splunk Processing Language (SPL) is an example of a pipelined commandlanguage in which a set of inputs or data is operated on by any numberof commands in a particular sequence. A sequence of commands, or commandsequence, can be formulated such that the order in which the commandsare arranged defines the order in which the commands are applied to aset of data or the results of an earlier executed command. For example,a first command in a command sequence can operate to search or filterfor specific data in particular set of data. The results of the firstcommand can then be passed to another command listed later in thecommand sequence for further processing.

In various embodiments, a query can be formulated as a command sequencedefined in a command line of a search UI. In some embodiments, a querycan be formulated as a sequence of SPL commands. Some or all of the SPLcommands in the sequence of SPL commands can be separated from oneanother by a pipe symbol “I”. In such embodiments, a set of data, suchas a set of events, can be operated on by a first SPL command in thesequence, and then a subsequent SPL command following a pipe symbol “I”after the first SPL command operates on the results produced by thefirst SPL command or other set of data, and so on for any additional SPLcommands in the sequence. As such, a query formulated using SPLcomprises a series of consecutive commands that are delimited by pipe“I” characters. The pipe character indicates to the system that theoutput or result of one command (to the left of the pipe) should be usedas the input for one of the subsequent commands (to the right of thepipe). This enables formulation of queries defined by a pipeline ofsequenced commands that refines or enhances the data at each step alongthe pipeline until the desired results are attained. Accordingly,various embodiments described herein can be implemented with SplunkProcessing Language (SPL) used in conjunction with the SPLUNK®ENTERPRISE system.

While a query can be formulated in many ways, a query can start with asearch command and one or more corresponding search terms at thebeginning of the pipeline. Such search terms can include any combinationof keywords, phrases, times, dates, Boolean expressions, fieldname-fieldvalue pairs, etc. that specify which results should be obtained from anindex. The results can then be passed as inputs into subsequent commandsin a sequence of commands by using, for example, a pipe character. Thesubsequent commands in a sequence can include directives for additionalprocessing of the results once it has been obtained from one or moreindexes. For example, commands may be used to filter unwantedinformation out of the results, extract more information, evaluate fieldvalues, calculate statistics, reorder the results, create an alert,create summary of the results, or perform some type of aggregationfunction. In some embodiments, the summary can include a graph, chart,metric, or other visualization of the data. An aggregation function caninclude analysis or calculations to return an aggregate value, such asan average value, a sum, a maximum value, a root mean square,statistical values, and the like.

Due to its flexible nature, use of a pipelined command language invarious embodiments is advantageous because it can perform “filtering”as well as “processing” functions. In other words, a single query caninclude a search command and search term expressions, as well asdata-analysis expressions. For example, a command at the beginning of aquery can perform a “filtering” step by retrieving a set of data basedon a condition (e.g., records associated with server response times ofless than 1 microsecond). The results of the filtering step can then bepassed to a subsequent command in the pipeline that performs a“processing” step (e.g. calculation of an aggregate value related to thefiltered events such as the average response time of servers withresponse times of less than 1 microsecond). Furthermore, the searchcommand can allow events to be filtered by keyword as well as fieldvalue criteria. For example, a search command can filter out all eventscontaining the word “warning” or filter out all events where a fieldvalue associated with a field “clientip” is “10.0.1.2.”

The results obtained or generated in response to a command in a querycan be considered a set of results data. The set of results data can bepassed from one command to another in any data format. In oneembodiment, the set of result data can be in the form of a dynamicallycreated table. Each command in a particular query can redefine the shapeof the table. In some implementations, an event retrieved from an indexin response to a query can be considered a row with a column for eachfield value. Columns contain basic information about the data and alsomay contain data that has been dynamically extracted at search time.

FIG. 6B provides a visual representation of the manner in which apipelined command language or query operates in accordance with thedisclosed embodiments. The query 630 can be inputted by the user into asearch. The query comprises a search, the results of which are piped totwo commands (namely, command 1 and command 2) that follow the searchstep.

Disk 622 represents the event data in the raw record data store.

When a user query is processed, a search step will precede other queriesin the pipeline in order to generate a set of events at block 640. Forexample, the query can comprise search terms “sourcetype=syslog ERROR”at the front of the pipeline as shown in FIG. 6B. Intermediate resultstable 624 shows fewer rows because it represents the subset of eventsretrieved from the index that matched the search terms“sourcetype=syslog ERROR” from search command 630. By way of furtherexample, instead of a search step, the set of events at the head of thepipeline may be generating by a call to a pre-existing inverted index(as will be explained later).

At block 642, the set of events generated in the first part of the querymay be piped to a query that searches the set of events for field-valuepairs or for keywords. For example, the second intermediate resultstable 626 shows fewer columns, representing the result of the topcommand, “top user” which summarizes the events into a list of the top10 users and displays the user, count, and percentage.

Finally, at block 644, the results of the prior stage can be pipelinedto another stage where further filtering or processing of the data canbe performed, e.g., preparing the data for display purposes, filteringthe data based on a condition, performing a mathematical calculationwith the data, etc. As shown in FIG. 6B, the “fields—percent” part ofcommand 630 removes the column that shows the percentage, thereby,leaving a final results table 628 without a percentage column. Indifferent embodiments, other query languages, such as the StructuredQuery Language (“SQL”), can be used to create a query. In someembodiments, each stage can correspond to a search phase or layer in aDAG. The processing performed in each stage can be handled by one ormore partitions allocated to each stage.

3.10. Field Extraction

The search head 210 allows users to search and visualize eventsgenerated from machine data received from homogenous data sources. Thesearch head 210 also allows users to search and visualize eventsgenerated from machine data received from heterogeneous data sources.The search head 210 includes various mechanisms, which may additionallyreside in an indexer 206, for processing a query. A query language maybe used to create a query, such as any suitable pipelined querylanguage. For example, Splunk Processing Language (SPL) can be utilizedto make a query. SPL is a pipelined search language in which a set ofinputs is operated on by a first command in a command line, and then asubsequent command following the pipe symbol “I” operates on the resultsproduced by the first command, and so on for additional commands. Otherquery languages, such as the Structured Query Language (“SQL”), can beused to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for fields in the events beingsearched. The search head 210 obtains extraction rules that specify howto extract a value for fields from an event. Extraction rules cancomprise regex rules that specify how to extract values for the fieldscorresponding to the extraction rules. In addition to specifying how toextract field values, the extraction rules may also include instructionsfor deriving a field value by performing a function on a characterstring or value retrieved by the extraction rule. For example, anextraction rule may truncate a character string or convert the characterstring into a different data format. In some cases, the query itself canspecify one or more extraction rules.

The search head 210 can apply the extraction rules to events that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the portions of machine datain the events and examining the data for one or more patterns ofcharacters, numbers, delimiters, etc., that indicate where the fieldbegins and, optionally, ends.

As mentioned above, and as will be described in greater detail belowwith reference to, inter alia, FIGS. 18-49 , some functionality of thesearch head or indexers can be handled by different components of thesystem or removed altogether. For example, in some cases, a querycoordinator or nodes use extraction rules to extract values for fieldsin the events being searched. The query coordinator or nodes obtainextraction rules that specify how to extract a value for fields from anevent, etc., and apply the extraction rules to events that it receivesfrom indexers, common storage, ingested data buffers, query accelerationdata stores, or other dataset sources.

FIG. 7A is a diagram of an example scenario where a common customeridentifier is found among log data received from three disparate datasources, in accordance with example embodiments. In this example, a usersubmits an order for merchandise using a vendor's shopping applicationprogram 701 running on the user's system. In this example, the order wasnot delivered to the vendor's server due to a resource exception at thedestination server that is detected by the middleware code 702. The userthen sends a message to the customer support server 703 to complainabout the order failing to complete. The three systems 701, 702, and 703are disparate systems that do not have a common logging format. Theorder application 701 sends log data 704 to the data intake and querysystem in one format, the middleware code 702 sends error log data 705in a second format, and the support server 703 sends log data 706 in athird format.

Using the log data received at one or more indexers 206 from the threesystems, the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of the systems.There is a semantic relationship between the customer ID field valuesgenerated by the three systems. The search head 210 requests events fromthe one or more indexers 206 to gather relevant events from the threesystems. The search head 210 then applies extraction rules to the eventsin order to extract field values that it can correlate. The search headmay apply a different extraction rule to each set of events from eachsystem when the event format differs among systems. In this example, theuser interface can display to the administrator the events correspondingto the common customer ID field values 707, 708, and 709, therebyproviding the administrator with insight into a customer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, avisualization (e.g., a graph or chart) generated from the values, andthe like.

The search system enables users to run queries against the stored datato retrieve events that meet criteria specified in a query, such ascontaining certain keywords or having specific values in defined fields.FIG. 7B illustrates the manner in which keyword searches and fieldsearches are processed in accordance with disclosed embodiments.

If a user inputs a search query into search bar 1401 that includes onlykeywords (also known as “tokens”), e.g., the keyword “error” or“warning”, the query search engine of the data intake and query systemsearches for those keywords directly in the event data 722 stored in theraw record data store. Note that while FIG. 7B only illustrates fourevents, the raw record data store (corresponding to data store 208 inFIG. 2 ) may contain records for millions of events.

As disclosed above, an indexer can optionally generate a keyword indexto facilitate fast keyword searching for event data. The indexerincludes the identified keywords in an index, which associates eachstored keyword with reference pointers to events containing that keyword(or to locations within events where that keyword is located, otherlocation identifiers, etc.). When an indexer subsequently receives akeyword-based query, the indexer can access the keyword index to quicklyidentify events containing the keyword. For example, if the keyword“HTTP” was indexed by the indexer at index time, and the user searchesfor the keyword “HTTP”, events 713 to 715 will be identified based onthe results returned from the keyword index. As noted above, the indexcontains reference pointers to the events containing the keyword, whichallows for efficient retrieval of the relevant events from the rawrecord data store.

If a user searches for a keyword that has not been indexed by theindexer, the data intake and query system would nevertheless be able toretrieve the events by searching the event data for the keyword in theraw record data store directly as shown in FIG. 7B. For example, if auser searches for the keyword “frank”, and the name “frank” has not beenindexed at index time, the DATA INTAKE AND QUERY system will search theevent data directly and return the first event 713. Note that whetherthe keyword has been indexed at index time or not, in both cases the rawdata with the events 712 is accessed from the raw data record store toservice the keyword search. In the case where the keyword has beenindexed, the index will contain a reference pointer that will allow fora more efficient retrieval of the event data from the data store. If thekeyword has not been indexed, the search engine will need to searchthrough all the records in the data store to service the search.

In most cases, however, in addition to keywords, a user's search willalso include fields. The term “field” refers to a location in the eventdata containing one or more values for a specific data item. Often, afield is a value with a fixed, delimited position on a line, or a nameand value pair, where there is a single value to each field name. Afield can also be multivalued, that is, it can appear more than once inan event and have a different value for each appearance, e.g., emailaddress fields. Fields are searchable by the field name or fieldname-value pairs. Some examples of fields are “clientip” for IPaddresses accessing a web server, or the “From” and “To” fields in emailaddresses.

By way of further example, consider the search, “status=404”. Thissearch query finds events with “status” fields that have a value of“404.” When the search is run, the search engine does not look forevents with any other “status” value. It also does not look for eventscontaining other fields that share “404” as a value. As a result, thesearch returns a set of results that are more focused than if “404” hadbeen used in the search string as part of a keyword search. Note alsothat fields can appear in events as “key=value” pairs such as“user_name=Bob.” But in most cases, field values appear in fixed,delimited positions without identifying keys. For example, the datastore may contain events where the “user_name” value always appears byitself after the timestamp as illustrated by the following string: “Nov.15 09:33:22 johnmedlock.”

The data intake and query system advantageously allows for search timefield extraction. In other words, fields can be extracted from the eventdata at search time using late-binding schema as opposed to at dataingestion time, which was a major limitation of the prior art systems.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

FIG. 7B illustrates the manner in which configuration files may be usedto configure custom fields at search time in accordance with thedisclosed embodiments. In response to receiving a search query, the dataintake and query system determines if the query references a “field.”For example, a query may request a list of events where the “clientip”field equals “127.0.0.1.” If the query itself does not specify anextraction rule and if the field is not a metadata field, e.g., time,host, source, source type, etc., then in order to determine anextraction rule, the search engine may, in one or more embodiments, needto locate configuration file 712 during the execution of the search asshown in FIG. 7B.

Configuration file 712 may contain extraction rules for all the variousfields that are not metadata fields, e.g., the “clientip” field. Theextraction rules may be inserted into the configuration file in avariety of ways. In some embodiments, the extraction rules can compriseregular expression rules that are manually entered in by the user.Regular expressions match patterns of characters in text and are usedfor extracting custom fields in text.

In one or more embodiments, as noted above, a field extractor may beconfigured to automatically generate extraction rules for certain fieldvalues in the events when the events are being created, indexed, orstored, or possibly at a later time. In one embodiment, a user may beable to dynamically create custom fields by highlighting portions of asample event that should be extracted as fields using a graphical userinterface. The system would then generate a regular expression thatextracts those fields from similar events and store the regularexpression as an extraction rule for the associated field in theconfiguration file 712.

In some embodiments, the indexers may automatically discover certaincustom fields at index time and the regular expressions for those fieldswill be automatically generated at index time and stored as part ofextraction rules in configuration file 712. For example, fields thatappear in the event data as “key=value” pairs may be automaticallyextracted as part of an automatic field discovery process. Note thatthere may be several other ways of adding field definitions toconfiguration files in addition to the methods discussed herein.

The search head 210 can apply the extraction rules derived fromconfiguration file 1402 to event data that it receives from indexers206. Indexers 206 may apply the extraction rules from the configurationfile to events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

In one more embodiments, the extraction rule in configuration file 712will also need to define the type or set of events that the rule appliesto. Because the raw record data store will contain events from multipleheterogeneous sources, multiple events may contain the same fields indifferent locations because of discrepancies in the format of the datagenerated by the various sources. Furthermore, certain events may notcontain a particular field at all. For example, event 719 also contains“clientip” field, however, the “clientip” field is in a different formatfrom events 713-715. To address the discrepancies in the format andcontent of the different types of events, the configuration file willalso need to specify the set of events that an extraction rule appliesto, e.g., extraction rule 716 specifies a rule for filtering by the typeof event and contains a regular expression for parsing out the fieldvalue. Accordingly, each extraction rule will pertain to only aparticular type of event. If a particular field, e.g., “clientip” occursin multiple events, each of those types of events would need its owncorresponding extraction rule in the configuration file 712 and each ofthe extraction rules would comprise a different regular expression toparse out the associated field value. The most common way to categorizeevents is by source type because events generated by a particular sourcecan have the same format.

The field extraction rules stored in configuration file 712 performsearch-time field extractions. For example, for a query that requests alist of events with source type “access_combined” where the “clientip”field equals “127.0.0.1,” the query search engine would first locate theconfiguration file 712 to retrieve extraction rule 716 that would allowit to extract values associated with the “clientip” field from the eventdata 720 “where the source type is “access_combined. After the“clientip” field has been extracted from all the events comprising the“clientip” field where the source type is “access_combined,” the querysearch engine can then execute the field criteria by performing thecompare operation to filter out the events where the “clientip” fieldequals “127.0.0.1.” In the example shown in FIG. 7B, events 713-715would be returned in response to the user query. In this manner, thesearch engine can service queries containing field criteria in additionto queries containing keyword criteria (as explained above).

The configuration file can be created during indexing. It may either bemanually created by the user or automatically generated with certainpredetermined field extraction rules. As discussed above, the events maybe distributed across several indexers, wherein each indexer may beresponsible for storing and searching a subset of the events containedin a corresponding data store. In a distributed indexer system, eachindexer would need to maintain a local copy of the configuration filethat is synchronized periodically across the various indexers.

The ability to add schema to the configuration file at search timeresults in increased efficiency. A user can create new fields at searchtime and simply add field definitions to the configuration file. As auser learns more about the data in the events, the user can continue torefine the late-binding schema by adding new fields, deleting fields, ormodifying the field extraction rules in the configuration file for usethe next time the schema is used by the system. Because the data intakeand query system maintains the underlying raw data and uses late-bindingschema for searching the raw data, it enables a user to continueinvestigating and learn valuable insights about the raw data long afterdata ingestion time.

The ability to add multiple field definitions to the configuration fileat search time also results in increased flexibility. For example,multiple field definitions can be added to the configuration file tocapture the same field across events generated by different sourcetypes. This allows the data intake and query system to search andcorrelate data across heterogeneous sources flexibly and efficiently.

Further, by providing the field definitions for the queried fields atsearch time, the configuration file 712 allows the record data store 712to be field searchable. In other words, the raw record data store 712can be searched using keywords as well as fields, wherein the fields aresearchable name/value pairings that distinguish one event from anotherand can be defined in configuration file 1402 using extraction rules. Incomparison to a search containing field names, a keyword search does notneed the configuration file and can search the event data directly asshown in FIG. 7B.

It should also be noted that any events filtered out by performing asearch-time field extraction using a configuration file can be furtherprocessed by directing the results of the filtering step to a processingstep using a pipelined search language. Using the prior example, a usercould pipeline the results of the compare step to an aggregate functionby asking the query search engine to count the number of events wherethe “clientip” field equals “127.0.0.1.”

As mentioned above, and as will be described in greater detail belowwith reference to, inter alia, FIGS. 18-49 , some functionality of thesearch head or indexers can be handled by different components of thesystem or removed altogether. For example, in some cases, the data isstored in a dataset source, which may be an indexer (or data storecontrolled by an indexer) or may be a different type of dataset source,such as a common storage or external data source. In addition, a querycoordinator or node can request events from the indexers or otherdataset source, apply extraction rules and correlate, automaticallydiscover certain custom fields, etc., as described above.

3.11. Example Search Screen

FIG. 8A is an interface diagram of an example user interface for asearch screen 800, in accordance with example embodiments. Search screen800 includes a search bar 802 that accepts user input in the form of asearch string. It also includes a time range picker 812 that enables theuser to specify a time range for the search. For historical searches(e.g., searches based on a particular historical time range), the usercan select a specific time range, or alternatively a relative timerange, such as “today,” “yesterday” or “last week.” For real-timesearches (e.g., searches whose results are based on data received inreal-time), the user can select the size of a time window to search forreal-time events. Search screen 800 also initially displays a “datasummary” dialog as is illustrated in FIG. 8B that enables the user toselect different sources for the events, such as by selecting specifichosts and log files.

After the search is executed, the search screen 800 in FIG. 8A candisplay the results through search results tabs 804, wherein searchresults tabs 804 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 8A displays a timeline graph 805 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. The events tab also displays anevents list 808 that enables a user to view the machine data in each ofthe returned events.

The events tab additionally displays a sidebar that is an interactivefield picker 806. The field picker 806 may be displayed to a user inresponse to the search being executed and allows the user to furtheranalyze the search results based on the fields in the events of thesearch results. The field picker 806 includes field names that referencefields present in the events in the search results. The field picker maydisplay any Selected Fields 820 that a user has pre-selected for display(e.g., host, source, sourcetype) and may also display any InterestingFields 822 that the system determines may be interesting to the userbased on pre-specified criteria (e.g., action, bytes, categoryid,clientip, date_hour, date_mday, date_minute, etc.). The field pickeralso provides an option to display field names for all the fieldspresent in the events of the search results using the All Fields control824.

Each field name in the field picker 806 has a value type identifier tothe left of the field name, such as value type identifier 826. A valuetype identifier identifies the type of value for the respective field,such as an “a” for fields that include literal values or a “#” forfields that include numerical values.

Each field name in the field picker also has a unique value count to theright of the field name, such as unique value count 828. The uniquevalue count indicates the number of unique values for the respectivefield in the events of the search results.

Each field name is selectable to view the events in the search resultsthat have the field referenced by that field name. For example, a usercan select the “host” field name, and the events shown in the eventslist 808 will be updated with events in the search results that have thefield that is reference by the field name “host.”

3.12. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge used to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.An object is defined by constraints and attributes. An object'sconstraints are search criteria that define the set of events to beoperated on by running a search having that search criteria at the timethe data model is selected. An object's attributes are the set of fieldsto be exposed for operating on that set of events generated by thesearch criteria.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Child objects inherit constraints andattributes from their parent objects and may have additional constraintsand attributes of their own. Child objects provide a way of filteringevents from parent objects. Because a child object may provide anadditional constraint in addition to the constraints it has inheritedfrom its parent object, the dataset it represents may be a subset of thedataset that its parent represents. For example, a first data modelobject may define a broad set of data pertaining to e-mail activitygenerally, and another data model object may define specific datasetswithin the broad dataset, such as a subset of the e-mail data pertainingspecifically to e-mails sent. For example, a user can simply select an“e-mail activity” data model object to access a dataset relating toe-mails generally (e.g., sent or received), or select an “e-mails sent”data model object (or data sub-model object) to access a datasetrelating to e-mails sent.

Because a data model object is defined by its constraints (e.g., a setof search criteria) and attributes (e.g., a set of fields), a data modelobject can be used to quickly search data to identify a set of eventsand to identify a set of fields to be associated with the set of events.For example, an “e-mails sent” data model object may specify a searchfor events relating to e-mails that have been sent, and specify a set offields that are associated with the events. Thus, a user can retrieveand use the “e-mails sent” data model object to quickly search sourcedata for events relating to sent e-mails, and may be provided with alisting of the set of fields relevant to the events in a user interfacescreen.

Examples of data models can include electronic mail, authentication,databases, intrusion detection, malware, application state, alerts,compute inventory, network sessions, network traffic, performance,audits, updates, vulnerabilities, etc. Data models and their objects canbe designed by knowledge managers in an organization, and they canenable downstream users to quickly focus on a specific set of data. Auser iteratively applies a model development tool (not shown in FIG. 8A)to prepare a query that defines a subset of events and assigns an objectname to that subset. A child subset is created by further limiting aquery that generated a parent subset.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. Pat. No.9,128,980, entitled “GENERATION OF A DATA MODEL APPLIED TO QUERIES”,issued on 8 Sep. 2015, and U.S. Pat. No. 9,589,012, entitled “GENERATIONOF A DATA MODEL APPLIED TO OBJECT QUERIES”, issued on 7 Mar. 2017, eachof which is hereby incorporated by reference in its entirety for allpurposes.

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In some embodiments, the data intake and query system 108 provides theuser with the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes. Datavisualizations also can be generated in a variety of formats, byreference to the data model. Reports, data visualizations, and datamodel objects can be saved and associated with the data model for futureuse. The data model object may be used to perform searches of otherdata.

FIGS. 9-15 are interface diagrams of example report generation userinterfaces, in accordance with example embodiments. The reportgeneration process may be driven by a predefined data model object, suchas a data model object defined and/or saved via a reporting applicationor a data model object obtained from another source. A user can load asaved data model object using a report editor. For example, the initialsearch query and fields used to drive the report editor may be obtainedfrom a data model object. The data model object that is used to drive areport generation process may define a search and a set of fields. Uponloading of the data model object, the report generation process mayenable a user to use the fields (e.g., the fields defined by the datamodel object) to define criteria for a report (e.g., filters, splitrows/columns, aggregates, etc.) and the search may be used to identifyevents (e.g., to identify events responsive to the search) used togenerate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 9 illustrates an example interactive data modelselection graphical user interface 900 of a report editor that displaysa listing of available data models 901. The user may select one of thedata models 902.

FIG. 10 illustrates an example data model object selection graphicaluser interface 1000 that displays available data objects 1001 for theselected data object model 902. The user may select one of the displayeddata model objects 1002 for use in driving the report generationprocess.

Once a data model object is selected by the user, a user interfacescreen 1100 shown in FIG. 11A may display an interactive listing ofautomatic field identification options 1101 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 1102, the “SelectedFields” option 1103, or the “Coverage” option (e.g., fields with atleast a specified % of coverage) 1104). If the user selects the “AllFields” option 1102, all of the fields identified from the events thatwere returned in response to an initial search query may be selected.That is, for example, all of the fields of the identified data modelobject fields may be selected. If the user selects the “Selected Fields”option 1103, only the fields from the fields of the identified datamodel object fields that are selected by the user may be used. If theuser selects the “Coverage” option 1104, only the fields of theidentified data model object fields meeting a specified coveragecriteria may be selected. A percent coverage may refer to the percentageof events returned by the initial search query that a given fieldappears in. Thus, for example, if an object dataset includes 10,000events returned in response to an initial search query, and the“avg_age” field appears in 854 of those 10,000 events, then the“avg_age” field would have a coverage of 8.54% for that object dataset.If, for example, the user selects the “Coverage” option and specifies acoverage value of 2%, only fields having a coverage value equal to orgreater than 2% may be selected. The number of fields corresponding toeach selectable option may be displayed in association with each option.For example, “97” displayed next to the “All Fields” option 1102indicates that 97 fields will be selected if the “All Fields” option isselected. The “3” displayed next to the “Selected Fields” option 1103indicates that 3 of the 97 fields will be selected if the “SelectedFields” option is selected. The “49” displayed next to the “Coverage”option 1104 indicates that 49 of the 97 fields (e.g., the 49 fieldshaving a coverage of 2% or greater) will be selected if the “Coverage”option is selected. The number of fields corresponding to the “Coverage”option may be dynamically updated based on the specified percent ofcoverage.

FIG. 11B illustrates an example graphical user interface screen 1105displaying the reporting application's “Report Editor” page. The screenmay display interactive elements for defining various elements of areport. For example, the page includes a “Filters” element 1106, a“Split Rows” element 1107, a “Split Columns” element 1108, and a “ColumnValues” element 1109. The page may include a list of search results1111. In this example, the Split Rows element 1107 is expanded,revealing a listing of fields 1110 that can be used to define additionalcriteria (e.g., reporting criteria). The listing of fields 1110 maycorrespond to the selected fields. That is, the listing of fields 1110may list only the fields previously selected, either automaticallyand/or manually by a user. FIG. 11C illustrates a formatting dialogue1112 that may be displayed upon selecting a field from the listing offields 1110. The dialogue can be used to format the display of theresults of the selection (e.g., label the column for the selected fieldto be displayed as “component”).

FIG. 11D illustrates an example graphical user interface screen 1105including a table of results 1113 based on the selected criteriaincluding splitting the rows by the “component” field. A column 1114having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row for aparticular field, such as the value “BucketMover” for the field“component”) occurs in the set of events responsive to the initialsearch query.

FIG. 12 illustrates an example graphical user interface screen 1200 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1201 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1202. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1206. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1203. A count of the number of successful purchases foreach product is displayed in column 1204. These statistics may beproduced by filtering the search results by the product name, findingall occurrences of a successful purchase in a field within the events,and generating a total of the number of occurrences. A sum of the totalsales is displayed in column 1205, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 13 illustrates an example graphical user interface 1300 thatdisplays a set of components and associated statistics 1301. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.), wherethe format of the graph may be selected using the user interfacecontrols 1302 along the left panel of the user interface 1300. FIG. 14illustrates an example of a bar chart visualization 1400 of an aspect ofthe statistical data 1301. FIG. 15 illustrates a scatter plotvisualization 1500 of an aspect of the statistical data 1301.

3.13. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally-processed data “on thefly” at search time using a late-binding schema, instead of storingpre-specified portions of the data in a database at ingestion time. Thisflexibility enables a user to see valuable insights, correlate data, andperform subsequent queries to examine interesting aspects of the datathat may not have been apparent at ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, the data intake and query system also employs anumber of unique acceleration techniques that have been developed tospeed up analysis operations performed at search time. These techniquesinclude: (1) performing search operations in parallel across multipleindexers; (2) using a keyword index; (3) using a high performanceanalytics store; and (4) accelerating the process of generating reports.These novel techniques are described in more detail below. Althoughdescribed as being performed by an indexer, it will be understood thatvarious components can be used to perform similar functionality. Forexample, nodes can perform any one or any combination of the searchfunctions described herein. In some cases, the nodes perform the searchfunctions based on instructions received from a query coordinator.

3.13.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 16 is an example search query receivedfrom a client and executed by search peers, in accordance with exampleembodiments. FIG. 16 illustrates how a search query 1602 received from aclient at a search head 210 can split into two phases, including: (1)subtasks 1604 (e.g., data retrieval or simple filtering) that may beperformed in parallel by indexers 206 for execution, and (2) a searchresults aggregation operation 1606 to be executed by the search headwhen the results are ultimately collected from the indexers.

During operation, upon receiving search query 1602, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 1602 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 1604, and then distributes searchquery 1604 to distributed indexers, which are also referred to as“search peers” or “peer indexers.” Note that search queries maygenerally specify search criteria or operations to be performed onevents that meet the search criteria. Search queries may also specifyfield names, as well as search criteria for the values in the fields oroperations to be performed on the values in the fields.

Moreover, the search head may distribute the full search query to thesearch peers as illustrated in FIG. 6A, or may alternatively distributea modified version (e.g., a more restricted version) of the search queryto the search peers. In this example, the indexers are responsible forproducing the results and sending them to the search head. After theindexers return the results to the search head, the search headaggregates the received results 1606 to form a single search result set.By executing the query in this manner, the system effectivelydistributes the computational operations across the indexers whileminimizing data transfers.

As mentioned above, and as will be described in greater detail belowwith reference to, inter alia, 18-49, some functionality of the searchhead or indexers can be handled by different components of the system orremoved altogether. For example, in some cases, the data is stored inone or more dataset sources, such as, but not limited to an indexer (ordata store controlled by an indexer), common storage, external datasource, ingested data buffer, query acceleration data store, etc. Inaddition, in some cases a query coordinator can aggregate results frommultiple indexers or nodes, perform an aggregation operation 1606,determine what, if any, portion of the operations of the search queryare to be performed locally the query coordinator, modify or translate asearch query for an indexer or other dataset source, distribute thequery to indexers, peers, or nodes, etc.

3.13.2. Keyword Index

As described above with reference to the flow charts in FIG. 5A, FIG.5B, and FIG. 6A, data intake and query system 108 can construct andmaintain one or more keyword indices to quickly identify eventscontaining specific keywords. This technique can greatly speed up theprocessing of queries involving specific keywords. As mentioned above,to build a keyword index, an indexer first identifies a set of keywords.Then, the indexer includes the identified keywords in an index, whichassociates each stored keyword with references to events containing thatkeyword, or to locations within events where that keyword is located.When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword. In some embodiments, a node or other components of the systemthat performs search operations can use the keyword index to identifyevents, etc.

3.13.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the events and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. Pat. No.9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCE ANALYTICS STOREWITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO AN EVENT QUERY”,issued on 8 Sep. 2015, and U.S. patent application Ser. No. 14/815,973,entitled “GENERATING AND STORING SUMMARIZATION TABLES FOR SETS OFSEARCHABLE EVENTS”, filed on 1 Aug. 2015, each of which is herebyincorporated by reference in its entirety for all purposes.

To speed up certain types of queries, e.g., frequently encounteredqueries or computationally intensive queries, some embodiments of system108 create a high performance analytics store, which is referred to as a“summarization table,” (also referred to as a “lexicon” or “invertedindex”) that contains entries for specific field-value pairs. Each ofthese entries keeps track of instances of a specific value in a specificfield in the event data and includes references to events containing thespecific value in the specific field. For example, an example entry inan inverted index can keep track of occurrences of the value “94107” ina “ZIP code” field of a set of events and the entry includes referencesto all of the events that contain the value “94107” in the ZIP codefield. Creating the inverted index data structure avoids needing toincur the computational overhead each time a statistical query needs tobe run on a frequently encountered field-value pair. In order toexpedite queries, in most embodiments, the search engine will employ theinverted index separate from the raw record data store to generateresponses to the received queries.

Note that the term “summarization table” or “inverted index” as usedherein is a data structure that may be generated by an indexer thatincludes at least field names and field values that have been extractedand/or indexed from event records. An inverted index may also includereference values that point to the location(s) in the field searchabledata store where the event records that include the field may be found.Also, an inverted index may be stored using well-known compressiontechniques to reduce its storage size.

Further, note that the term “reference value” (also referred to as a“posting value”) as used herein is a value that references the locationof a source record in the field searchable data store. In someembodiments, the reference value may include additional informationabout each record, such as timestamps, record size, meta-data, or thelike. Each reference value may be a unique identifier which may be usedto access the event data directly in the field searchable data store. Insome embodiments, the reference values may be ordered based on eachevent record's timestamp. For example, if numbers are used asidentifiers, they may be sorted so event records having a latertimestamp always have a lower valued identifier than event records withan earlier timestamp, or vice-versa. Reference values are often includedin inverted indexes for retrieving and/or identifying event records.

In one or more embodiments, an inverted index is generated in responseto a user-initiated collection query. The term “collection query” asused herein refers to queries that include commands that generatesummarization information and inverted indexes (or summarization tables)from event records stored in the field searchable data store.

Note that a collection query is a special type of query that can beuser-generated and is used to create an inverted index. A collectionquery is not the same as a query that is used to call up or invoke apre-existing inverted index. In one or more embodiment, a query cancomprise an initial step that calls up a pre-generated inverted index onwhich further filtering and processing can be performed. For example,referring back to FIG. 13 , a set of events generated at block 1320 byeither using a “collection” query to create a new inverted index or bycalling up a pre-generated inverted index. A query with severalpipelined steps will start with a pre-generated index to accelerate thequery.

FIG. 7C illustrates the manner in which an inverted index is created andused in accordance with the disclosed embodiments. As shown in FIG. 7C,an inverted index 722 can be created in response to a user-initiatedcollection query using the event data 723 stored in the raw record datastore. For example, a non-limiting example of a collection query mayinclude “collect clientip=127.0.0.1” which may result in an invertedindex 722 being generated from the event data 723 as shown in FIG. 7C.Each entry in inverted index 722 includes an event reference value thatreferences the location of a source record in the field searchable datastore. The reference value may be used to access the original eventrecord directly from the field searchable data store.

In one or more embodiments, if one or more of the queries is acollection query, the responsive indexers may generate summarizationinformation based on the fields of the event records located in thefield searchable data store. In at least one of the various embodiments,one or more of the fields used in the summarization information may belisted in the collection query and/or they may be determined based onterms included in the collection query. For example, a collection querymay include an explicit list of fields to summarize. Or, in at least oneof the various embodiments, a collection query may include terms orexpressions that explicitly define the fields, e.g., using regex rules.In FIG. 7C, prior to running the collection query that generates theinverted index 722, the field name “clientip” may need to be defined ina configuration file by specifying the “access_combined” source type anda regular expression rule to parse out the client IP address.Alternatively, the collection query may contain an explicit definitionfor the field name “clientip” which may obviate the need to referencethe configuration file at search time.

In one or more embodiments, collection queries may be saved andscheduled to run periodically. These scheduled collection queries mayperiodically update the summarization information corresponding to thequery. For example, if the collection query that generates invertedindex 722 is scheduled to run periodically, one or more indexers wouldperiodically search through the relevant buckets to update invertedindex 722 with event data for any new events with the “clientip” valueof “127.0.0.1.”

In some embodiments, the inverted indexes that include fields, values,and reference value (e.g., inverted index 722) for event records may beincluded in the summarization information provided to the user. In otherembodiments, a user may not be interested in specific fields and valuescontained in the inverted index, but may need to perform a statisticalquery on the data in the inverted index. For example, referencing theexample of FIG. 7C rather than viewing the fields within summarizationtable 722, a user may want to generate a count of all client requestsfrom IP address “127.0.0.1.” In this case, the search engine wouldsimply return a result of “4” rather than including details about theinverted index 722 in the information provided to the user.

The pipelined search language, e.g., SPL of the SPLUNK® ENTERPRISEsystem can be used to pipe the contents of an inverted index to astatistical query using the “stats” command for example. A “stats” queryrefers to queries that generate result sets that may produce aggregateand statistical results from event records, e.g., average, mean, max,min, rms, etc. Where sufficient information is available in an invertedindex, a “stats” query may generate their result sets rapidly from thesummarization information available in the inverted index rather thandirectly scanning event records. For example, the contents of invertedindex 722 can be pipelined to a stats query, e.g., a “count” functionthat counts the number of entries in the inverted index and returns avalue of “4.” In this way, inverted indexes may enable various statsqueries to be performed absent scanning or search the event records.Accordingly, this optimization technique enables the system to quicklyprocess queries that seek to determine how many events have a particularvalue for a particular field. To this end, the system can examine theentry in the inverted index to count instances of the specific value inthe field without having to go through the individual events or performdata extractions at search time.

In some embodiments, the system maintains a separate inverted index foreach of the above-described time-specific buckets that stores events fora specific time range. A bucket-specific inverted index includes entriesfor specific field-value combinations that occur in events in thespecific bucket. Alternatively, the system can maintain a separateinverted index for each indexer. The indexer-specific inverted indexincludes entries for the events in a data store that are managed by thespecific indexer. Indexer-specific inverted indexes may also bebucket-specific. In at least one or more embodiments, if one or more ofthe queries is a stats query, each indexer may generate a partial resultset from previously generated summarization information. The partialresult sets may be returned to the search head that received the queryand combined into a single result set for the query

As mentioned above, the inverted index can be populated by running aperiodic query that scans a set of events to find instances of aspecific field-value combination, or alternatively instances of allfield-value combinations for a specific field. A periodic query can beinitiated by a user, or can be scheduled to occur automatically atspecific time intervals. A periodic query can also be automaticallylaunched in response to a query that asks for a specific field-valuecombination. In some embodiments, if summarization information is absentfrom an indexer that includes responsive event records, further actionsmay be taken, such as, the summarization information may generated onthe fly, warnings may be provided the user, the collection queryoperation may be halted, the absence of summarization information may beignored, or the like, or combination thereof.

In one or more embodiments, an inverted index may be set up to updatecontinually. For example, the query may ask for the inverted index toupdate its result periodically, e.g., every hour. In such instances, theinverted index may be a dynamic data structure that is regularly updatedto include information regarding incoming events.

In some cases, e.g., where a query is executed before an inverted indexupdates, when the inverted index may not cover all of the events thatare relevant to a query, the system can use the inverted index to obtainpartial results for the events that are covered by inverted index, butmay also have to search through other events that are not covered by theinverted index to produce additional results on the fly. In other words,an indexer would need to search through event data on the data store tosupplement the partial results. These additional results can then becombined with the partial results to produce a final set of results forthe query. Note that in typical instances where an inverted index is notcompletely up to date, the number of events that an indexer would needto search through to supplement the results from the inverted indexwould be relatively small. In other words, the search to get the mostrecent results can be quick and efficient because only a small number ofevent records will be searched through to supplement the informationfrom the inverted index. The inverted index and associated techniquesare described in more detail in U.S. Pat. No. 8,682,925, entitled“DISTRIBUTED HIGH PERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014,U.S. Pat. No. 9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCEANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO ANEVENT QUERY”, filed on 31 Jan. 2014, and U.S. patent application Ser.No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROL DEVICE”, filed on21 Feb. 2014, each of which is hereby incorporated by reference in itsentirety. In some cases, the inverted indexes can be made available, aspart of a common storage, to nodes or other components of the systemthat perform search operations.

3.13.4. Extracting Event Data Using Posting

In one or more embodiments, if the system needs to process all eventsthat have a specific field-value combination, the system can use thereferences in the inverted index entry to directly access the events toextract further information without having to search all of the eventsto find the specific field-value combination at search time. In otherwords, the system can use the reference values to locate the associatedevent data in the field searchable data store and extract furtherinformation from those events, e.g., extract further field values fromthe events for purposes of filtering or processing or both.

The information extracted from the event data using the reference valuescan be directed for further filtering or processing in a query using thepipeline search language. The pipelined search language will, in oneembodiment, include syntax that can direct the initial filtering step ina query to an inverted index. In one embodiment, a user would includesyntax in the query that explicitly directs the initial searching orfiltering step to the inverted index.

Referencing the example in FIG. 15 , if the user determines that sheneeds the user id fields associated with the client requests from IPaddress “127.0.0.1,” instead of incurring the computational overhead ofperforming a brand new search or re-generating the inverted index withan additional field, the user can generate a query that explicitlydirects or pipes the contents of the already generated inverted index1502 to another filtering step requesting the user ids for the entriesin inverted index 1502 where the server response time is greater than“0.0900” microseconds. The search engine would use the reference valuesstored in inverted index 722 to retrieve the event data from the fieldsearchable data store, filter the results based on the “response time”field values and, further, extract the user id field from the resultingevent data to return to the user. In the present instance, the user ids“frank” and “carlos” would be returned to the user from the generatedresults table 722.

In one embodiment, the same methodology can be used to pipe the contentsof the inverted index to a processing step. In other words, the user isable to use the inverted index to efficiently and quickly performaggregate functions on field values that were not part of the initiallygenerated inverted index. For example, a user may want to determine anaverage object size (size of the requested gif) requested by clientsfrom IP address “127.0.0.1.” In this case, the search engine would againuse the reference values stored in inverted index 722 to retrieve theevent data from the field searchable data store and, further, extractthe object size field values from the associated events 731, 732, 733and 734. Once, the corresponding object sizes have been extracted (i.e.2326, 2900, 2920, and 5000), the average can be computed and returned tothe user.

In one embodiment, instead of explicitly invoking the inverted index ina user-generated query, e.g., by the use of special commands or syntax,the SPLUNK® ENTERPRISE system can be configured to automaticallydetermine if any prior-generated inverted index can be used to expeditea user query. For example, the user's query may request the averageobject size (size of the requested gif) requested by clients from IPaddress “127.0.0.1.” without any reference to or use of inverted index722. The search engine, in this case, would automatically determine thatan inverted index 722 already exists in the system that could expeditethis query. In one embodiment, prior to running any search comprising afield-value pair, for example, a search engine may search though all theexisting inverted indexes to determine if a pre-generated inverted indexcould be used to expedite the search comprising the field-value pair.Accordingly, the search engine would automatically use the pre-generatedinverted index, e.g., index 722 to generate the results without anyuser-involvement that directs the use of the index.

Using the reference values in an inverted index to be able to directlyaccess the event data in the field searchable data store and extractfurther information from the associated event data for further filteringand processing is highly advantageous because it avoids incurring thecomputation overhead of regenerating the inverted index with additionalfields or performing a new search.

The data intake and query system includes one or more forwarders thatreceive raw machine data from a variety of input data sources, and oneor more indexers that process and store the data in one or more datastores. By distributing events among the indexers and data stores, theindexers can analyze events for a query in parallel. In one or moreembodiments, a multiple indexer implementation of the search systemwould maintain a separate and respective inverted index for each of theabove-described time-specific buckets that stores events for a specifictime range. A bucket-specific inverted index includes entries forspecific field-value combinations that occur in events in the specificbucket. As explained above, a search head would be able to correlate andsynthesize data from across the various buckets and indexers.

This feature advantageously expedites searches because instead ofperforming a computationally intensive search in a centrally locatedinverted index that catalogues all the relevant events, an indexer isable to directly search an inverted index stored in a bucket associatedwith the time-range specified in the query. This allows the search to beperformed in parallel across the various indexers. Further, if the queryrequests further filtering or processing to be conducted on the eventdata referenced by the locally stored bucket-specific inverted index,the indexer is able to simply access the event records stored in theassociated bucket for further filtering and processing instead ofneeding to access a central repository of event records, which woulddramatically add to the computational overhead.

In one embodiment, there may be multiple buckets associated with thetime-range specified in a query. If the query is directed to an invertedindex, or if the search engine automatically determines that using aninverted index would expedite the processing of the query, the indexerswill search through each of the inverted indexes associated with thebuckets for the specified time-range. This feature allows the HighPerformance Analytics Store to be scaled easily.

In certain instances, where a query is executed before a bucket-specificinverted index updates, when the bucket-specific inverted index may notcover all of the events that are relevant to a query, the system can usethe bucket-specific inverted index to obtain partial results for theevents that are covered by bucket-specific inverted index, but may alsohave to search through the event data in the bucket associated with thebucket-specific inverted index to produce additional results on the fly.In other words, an indexer would need to search through event datastored in the bucket (that was not yet processed by the indexer for thecorresponding inverted index) to supplement the partial results from thebucket-specific inverted index.

FIG. 7D presents a flowchart illustrating how an inverted index in apipelined search query can be used to determine a set of event data thatcan be further limited by filtering or processing in accordance with thedisclosed embodiments.

At block 742, a query is received by a data intake and query system. Insome embodiments, the query can be receive as a user generated queryentered into search bar of a graphical user search interface. The searchinterface also includes a time range control element that enablesspecification of a time range for the query.

At block 744, an inverted index is retrieved. Note, that the invertedindex can be retrieved in response to an explicit user search commandinputted as part of the user generated query. Alternatively, the searchengine can be configured to automatically use an inverted index if itdetermines that using the inverted index would expedite the servicing ofthe user generated query. Each of the entries in an inverted index keepstrack of instances of a specific value in a specific field in the eventdata and includes references to events containing the specific value inthe specific field. In order to expedite queries, in most embodiments,the search engine will employ the inverted index separate from the rawrecord data store to generate responses to the received queries.

At block 746, the query engine determines if the query contains furtherfiltering and processing steps. If the query contains no furthercommands, then, in one embodiment, summarization information can beprovided to the user at block 754.

If, however, the query does contain further filtering and processingcommands, then at block 750, the query engine determines if the commandsrelate to further filtering or processing of the data extracted as partof the inverted index or whether the commands are directed to using theinverted index as an initial filtering step to further filter andprocess event data referenced by the entries in the inverted index. Ifthe query can be completed using data already in the generated invertedindex, then the further filtering or processing steps, e.g., a “count”number of records function, “average” number of records per hour etc.are performed and the results are provided to the user at block 752.

If, however, the query references fields that are not extracted in theinverted index, then the indexers will access event data pointed to bythe reference values in the inverted index to retrieve any furtherinformation required at block 756. Subsequently, any further filteringor processing steps are performed on the fields extracted directly fromthe event data and the results are provided to the user at step 758.

As described throughout, it will be understood that although describedas being performed by an indexer, these functions can be performed byanother component of the system, such as a query coordinator or node.For example, nodes can use inverted indexes to identify relevant data,etc. The inverted indexes can be stored with buckets in a commonstorage, etc.

3.13.5. Accelerating Report Generation

In some embodiments, a data server system such as the data intake andquery system can accelerate the process of periodically generatingupdated reports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on theseadditional events. Then, the results returned by this query on theadditional events, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer events needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety for all purposes.

3.14. Security Features

The data intake and query system provides various schemas, dashboards,and visualizations that simplify developers' tasks to createapplications with additional capabilities. One such application is thean enterprise security application, such as SPLUNK® ENTERPRISE SECURITY,which performs monitoring and alerting operations and includes analyticsto facilitate identifying both known and unknown security threats basedon large volumes of data stored by the data intake and query system. Theenterprise security application provides the security practitioner withvisibility into security-relevant threats found in the enterpriseinfrastructure by capturing, monitoring, and reporting on data fromenterprise security devices, systems, and applications. Through the useof the data intake and query system searching and reportingcapabilities, the enterprise security application provides a top-downand bottom-up view of an organization's security posture.

The enterprise security application leverages the data intake and querysystem search-time normalization techniques, saved searches, andcorrelation searches to provide visibility into security-relevantthreats and activity and generate notable events for tracking. Theenterprise security application enables the security practitioner toinvestigate and explore the data to find new or unknown threats that donot follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemslack the infrastructure to effectively store and analyze large volumesof security-related data. Traditional SIEM systems typically use fixedschemas to extract data from pre-defined security-related fields at dataingestion time and store the extracted data in a relational database.This traditional data extraction process (and associated reduction indata size) that occurs at data ingestion time inevitably hampers futureincident investigations that may need original data to determine theroot cause of a security issue, or to detect the onset of an impendingsecurity threat.

In contrast, the enterprise security application system stores largevolumes of minimally-processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the enterprise security application provides pre-specified schemas forextracting relevant values from the different types of security-relatedevents and enables a user to define such schemas.

The enterprise security application can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. Pat. No. 9,215,240, entitled “INVESTIGATIVE AND DYNAMIC DETECTIONOF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS IN BIG DATA”, issuedon 15 Dec. 2015, U.S. Pat. No. 9,173,801, entitled “GRAPHIC DISPLAY OFSECURITY THREATS BASED ON INDICATIONS OF ACCESS TO NEWLY REGISTEREDDOMAINS”, issued on 3 Nov. 2015, U.S. Pat. No. 9,248,068, entitled“SECURITY THREAT DETECTION OF NEWLY REGISTERED DOMAINS”, issued on 2Feb. 2016, U.S. Pat. No. 9,426,172, entitled “SECURITY THREAT DETECTIONUSING DOMAIN NAME ACCESSES”, issued on 23 Aug. 2016, and U.S. Pat. No.9,432,396, entitled “SECURITY THREAT DETECTION USING DOMAIN NAMEREGISTRATIONS”, issued on 30 Aug. 2016, each of which is herebyincorporated by reference in its entirety for all purposes.Security-related information can also include malware infection data andsystem configuration information, as well as access control information,such as login/logout information and access failure notifications. Thesecurity-related information can originate from various sources within adata center, such as hosts, virtual machines, storage devices andsensors. The security-related information can also originate fromvarious sources in a network, such as routers, switches, email servers,proxy servers, gateways, firewalls and intrusion-detection systems.

During operation, the enterprise security application facilitatesdetecting “notable events” that are likely to indicate a securitythreat. A notable event represents one or more anomalous incidents, theoccurrence of which can be identified based on one or more events (e.g.,time stamped portions of raw machine data) fulfilling pre-specifiedand/or dynamically-determined (e.g., based on machine-learning) criteriadefined for that notable event. Examples of notable events include therepeated occurrence of an abnormal spike in network usage over a periodof time, a single occurrence of unauthorized access to system, a hostcommunicating with a server on a known threat list, and the like. Thesenotable events can be detected in a number of ways, such as: (1) a usercan notice a correlation in events and can manually identify that acorresponding group of one or more events amounts to a notable event; or(2) a user can define a “correlation search” specifying criteria for anotable event, and every time one or more events satisfy the criteria,the application can indicate that the one or more events correspond to anotable event; and the like. A user can alternatively select apre-defined correlation search provided by the application. Note thatcorrelation searches can be run continuously or at regular intervals(e.g., every hour) to search for notable events. Upon detection, notableevents can be stored in a dedicated “notable events index,” which can besubsequently accessed to generate various visualizations containingsecurity-related information. Also, alerts can be generated to notifysystem operators when important notable events are discovered.

The enterprise security application provides various visualizations toaid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 17A illustrates anexample key indicators view 1700 that comprises a dashboard, which candisplay a value 1701, for various security-related metrics, such asmalware infections 1702. It can also display a change in a metric value1703, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 1700 additionallydisplays a histogram panel 1704 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 17B illustrates an example incident review dashboard 1710 thatincludes a set of incident attribute fields 1711 that, for example,enables a user to specify a time range field 1712 for the displayedevents. It also includes a timeline 1713 that graphically illustratesthe number of incidents that occurred in time intervals over theselected time range. It additionally displays an events list 1714 thatenables a user to view a list of all of the notable events that matchthe criteria in the incident attributes fields 1711. To facilitateidentifying patterns among the notable events, each notable event can beassociated with an urgency value (e.g., low, medium, high, critical),which is indicated in the incident review dashboard. The urgency valuefor a detected event can be determined based on the severity of theevent and the priority of the system component associated with theevent.

3.15. Data Center Monitoring

As mentioned above, the data intake and query platform provides variousfeatures that simplify the developers' task to create variousapplications. One such application is a virtual machine monitoringapplication, such as SPLUNK® APP FOR VMWARE® that provides operationalvisibility into granular performance metrics, logs, tasks and events,and topology from hosts, virtual machines and virtual centers. Itempowers administrators with an accurate real-time picture of the healthof the environment, proactively identifying performance and capacitybottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the virtual machine monitoring application stores largevolumes of minimally processed machine data, such as performanceinformation and log data, at ingestion time for later retrieval andanalysis at search time when a live performance issue is beinginvestigated. In addition to data obtained from various log files, thisperformance-related information can include values for performancemetrics obtained through an application programming interface (API)provided as part of the vSphere Hypervisor™ system distributed byVMware, Inc. of Palo Alto, Calif. For example, these performance metricscan include: (1) CPU-related performance metrics; (2) disk-relatedperformance metrics; (3) memory-related performance metrics; (4)network-related performance metrics; (5) energy-usage statistics; (6)data-traffic-related performance metrics; (7) overall systemavailability performance metrics; (8) cluster-related performancemetrics; and (9) virtual machine performance statistics. Suchperformance metrics are described in U.S. patent application Ser. No.14/167,316, entitled “CORRELATION FOR USER-SELECTED TIME RANGES OFVALUES FOR PERFORMANCE METRICS OF COMPONENTS IN ANINFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROM THATINFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, and which ishereby incorporated by reference in its entirety for all purposes.

To facilitate retrieving information of interest from performance dataand log files, the virtual machine monitoring application providespre-specified schemas for extracting relevant values from differenttypes of performance-related events, and also enables a user to definesuch schemas.

The virtual machine monitoring application additionally provides variousvisualizations to facilitate detecting and diagnosing the root cause ofperformance problems. For example, one such visualization is a“proactive monitoring tree” that enables a user to easily view andunderstand relationships among various factors that affect theperformance of a hierarchically structured computing system. Thisproactive monitoring tree enables a user to easily navigate thehierarchy by selectively expanding nodes representing various entities(e.g., virtual centers or computing clusters) to view performanceinformation for lower-level nodes associated with lower-level entities(e.g., virtual machines or host systems). Example node-expansionoperations are illustrated in FIG. 17C, wherein nodes 1733 and 1734 areselectively expanded. Note that nodes 1731-1739 can be displayed usingdifferent patterns or colors to represent different performance states,such as a critical state, a warning state, a normal state or anunknown/offline state. The ease of navigation provided by selectiveexpansion in combination with the associated performance-stateinformation enables a user to quickly diagnose the root cause of aperformance problem. The proactive monitoring tree is described infurther detail in U.S. Pat. No. 9,185,007, entitled “PROACTIVEMONITORING TREE WITH SEVERITY STATE SORTING”, issued on 10 Nov. 2015,and U.S. Pat. No. 9,426,045, also entitled “PROACTIVE MONITORING TREEWITH SEVERITY STATE SORTING”, issued on 23 Aug. 2016, each of which ishereby incorporated by reference in its entirety for all purposes.

The virtual machine monitoring application also provides a userinterface that enables a user to select a specific time range and thenview heterogeneous data comprising events, log data, and associatedperformance metrics for the selected time range. For example, the screenillustrated in FIG. 17D displays a listing of recent “tasks and events”and a listing of recent “log entries” for a selected time range above aperformance-metric graph for “average CPU core utilization” for theselected time range. Note that a user is able to operate pull-down menus1742 to selectively display different performance metric graphs for theselected time range. This enables the user to correlate trends in theperformance-metric graph with corresponding event and log data toquickly determine the root cause of a performance problem. This userinterface is described in more detail in U.S. patent application Ser.No. 14/167,316, entitled “CORRELATION FOR USER-SELECTED TIME RANGES OFVALUES FOR PERFORMANCE METRICS OF COMPONENTS IN ANINFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROM THATINFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, and which ishereby incorporated by reference in its entirety for all purposes.

3.16. It Service Monitoring

As previously mentioned, the data intake and query platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is an IT monitoring application, such as SPLUNK® ITSERVICE INTELLIGENCE™, which performs monitoring and alertingoperations. The IT monitoring application also includes analytics tohelp an analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the data intake and query system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related events. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, an IT monitoring application system stores large volumes ofminimally-processed service-related data at ingestion time for laterretrieval and analysis at search time, to perform regular monitoring, orto investigate a service issue. To facilitate this data retrievalprocess, the IT monitoring application enables a user to define an IToperations infrastructure from the perspective of the services itprovides. In this service-centric approach, a service such as corporatee-mail may be defined in terms of the entities employed to provide theservice, such as host machines and network devices. Each entity isdefined to include information for identifying all of the events thatpertains to the entity, whether produced by the entity itself or byanother machine, and considering the many various ways the entity may beidentified in machine data (such as by a URL, an IP address, or machinename). The service and entity definitions can organize events around aservice so that all of the events pertaining to that service can beeasily identified. This capability provides a foundation for theimplementation of Key Performance Indicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the IT monitoring application. Each KPI measures an aspect ofservice performance at a point in time or over a period of time (aspectKPI's). Each KPI is defined by a search query that derives a KPI valuefrom the machine data of events associated with the entities thatprovide the service. Information in the entity definitions may be usedto identify the appropriate events at the time a KPI is defined orwhenever a KPI value is being determined. The KPI values derived overtime may be stored to build a valuable repository of current andhistorical performance information for the service, and the repository,itself, may be subject to search query processing. Aggregate KPIs may bedefined to provide a measure of service performance calculated from aset of service aspect KPI values; this aggregate may even be takenacross defined timeframes and/or across multiple services. A particularservice may have an aggregate KPI derived from substantially all of theaspect KPI's of the service to indicate an overall health score for theservice.

The IT monitoring application facilitates the production of meaningfulaggregate KPI's through a system of KPI thresholds and state values.Different KPI definitions may produce values in different ranges, and sothe same value may mean something very different from one KPI definitionto another. To address this, the IT monitoring application implements atranslation of individual KPI values to a common domain of “state”values. For example, a KPI range of values may be 1-100, or 50-275,while values in the state domain may be ‘critical,’ warning,‘normal,’and ‘informational’.. Thresholds associated with a particularKPI definition determine ranges of values for that KPI that correspondto the various state values. In one case, KPI values 95-100 may be setto correspond to ‘critical’ in the state domain. KPI values fromdisparate KPI's can be processed uniformly once they are translated intothe common state values using the thresholds. For example, “normal 80%of the time” can be applied across various KPI's. To provide meaningfulaggregate KPI's, a weighting value can be assigned to each KPI so thatits influence on the calculated aggregate KPI value is increased ordecreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. The IT monitoring application can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in the IT monitoring application can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in the IT monitoring applicationcan also be created and updated by an import of tabular data (asrepresented in a CSV, another delimited file, or a search query resultset). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in the IT monitoring application can also be associated witha service by means of a service definition rule. Processing the ruleresults in the matching entity definitions being associated with theservice definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, the IT monitoring application can recognize notableevents that may indicate a service performance problem or othersituation of interest. These notable events can be recognized by a“correlation search” specifying trigger criteria for a notable event:every time KPI values satisfy the criteria, the application indicates anotable event. A severity level for the notable event may also bespecified. Furthermore, when trigger criteria are satisfied, thecorrelation search may additionally or alternatively cause a serviceticket to be created in an IT service management (ITSM) system, such asa systems available from Service Now, Inc., of Santa Clara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of events and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. The IT monitoringapplication provides a service monitoring interface suitable as the homepage for ongoing IT service monitoring. The interface is appropriate forsettings such as desktop use or for a wall-mounted display in a networkoperations center (NOC). The interface may prominently display aservices health section with tiles for the aggregate KPI's indicatingoverall health for defined services and a general KPI section with tilesfor KPI's related to individual service aspects. These tiles may displayKPI information in a variety of ways, such as by being colored andordered according to factors like the KPI state value. They also can beinteractive and navigate to visualizations of more detailed KPIinformation.

The IT monitoring application provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

The IT monitoring application provides a visualization showing detailedtime-series information for multiple KPI's in parallel graph lanes. Thelength of each lane can correspond to a uniform time range, while thewidth of each lane may be automatically adjusted to fit the displayedKPI data. Data within each lane may be displayed in a user selectablestyle, such as a line, area, or bar chart. During operation a user mayselect a position in the time range of the graph lanes to activate laneinspection at that point in time. Lane inspection may display anindicator for the selected time across the graph lanes and display theKPI value associated with that point in time for each of the graphlanes. The visualization may also provide navigation to an interface fordefining a correlation search, using information from the visualizationto pre-populate the definition.

The IT monitoring application provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

The IT monitoring application provides pre-specified schemas forextracting relevant values from the different types of service-relatedevents. It also enables a user to define such schemas.

4.0. Data Fabric Service (DFS)

The capabilities of a data intake and query system are typically limitedto resources contained within that system. For example, the data intakeand query system has search and analytics capabilities that are limitedin scope to the indexers responsible for storing and searching a subsetof events contained in their corresponding internal data stores.

Even if a data intake and query system has access to external datastores that may include data relevant to a query, the data intake andquery system typically has limited capabilities to process thecombination of partial search results from the indexers and externaldata sources to produce comprehensive search results. In particular, thesearch head of a data intake and query system may retrieve partialsearch results from external data systems over a network. The searchhead may also retrieve partial results from its indexers, and combinethose partial search results with the partial results of the externaldata sources to produce final results for a query.

For example, the search head can implement map-reduce techniques, whereeach data source returns partial search results and the search head cancombine the partial search results to produce the final results of aquery. However, obtaining results in this manner from distributed datasystems including internal data stores and external data stores haslimited value because the search head can act as a bottleneck forprocessing complex search queries on distributed data systems. Thebottleneck effect at the search head worsens as the number ofdistributed data systems increases. Furthermore, even without processingqueries on distributed data systems, the search head 210 and theindexers 206 can act as bottlenecks due to the number of queriesreceived by the data intake and query system 108 and the amount ofprocessing done by the indexers during data ingestion, indexing, andsearch.

Embodiments of the disclosed data fabric service (DFS) system 3301overcome the aforementioned drawbacks by expanding on the capabilitiesof a data intake and query system to enable application of a queryacross distributed data systems, which may also be referred to asdataset sources, including internal data stores coupled to indexers(illustrated in FIG. 33 ), external data stores coupled to the dataintake and query system over a network (illustrated in FIGS. 33, 46, 48), common storage (illustrated in FIGS. 46, 48 ), query accelerationdata stores (e.g., query acceleration data store 3308 illustrated inFIGS. 33, 46, 48 ), ingested data buffers (illustrated in FIG. 48 ) thatinclude ingested streaming data. Moreover, the disclosed embodiments arescalable to accommodate application of a query on a growing number ofdiverse data systems.

In certain embodiments, the disclosed DFS system extends thecapabilities of the data intake and query system and mitigates thebottleneck effect at the search head by including one or more querycoordinators communicatively coupled to worker nodes distributed in abig data ecosystem. In some embodiments, the worker nodes can becommunicatively coupled to the various dataset sources (e.g., indexers,common storage, external data systems that contain external data stores,ingested data buffers, query acceleration data stores, etc.)

The data intake and query system can receive a query input by a user ata client device via a search head. The search head can coordinate with asearch process master and/or one or more query coordinators (the searchprocess master and query coordinators can collectively referred to as asearch process service) to execute a search scheme applied to one ormore dataset sources (e.g., indexers, common storage, ingested databuffer, query acceleration data store, external data stores, etc.). Theworker nodes can collect, process, and aggregate the partial resultsfrom the dataset sources, and transfer the aggregate results to a querycoordinator. In some embodiments, the query coordinator can operate onthe aggregate results, and send finalized results to the search head,which can render the results of the query on a display device.

Hence, the search head in conjunction with the search process master andquery coordinator(s) can apply a query to any one or more of thedistributed dataset sources. The worker nodes can act in accordance withthe instructions received by a query coordinator to obtain relevantdatasets from the different dataset sources, process the datasets,aggregate the partial results of processing the different datasets, andcommunicate the aggregated results to the query coordinator, orelsewhere. In other words, the search head of the data intake and querysystem can offload at least some query processing to the querycoordinator and worker nodes, to both obtain the datasets from thedataset sources and aggregate the results of processing the differentdatasets. This system is scalable to accommodate any number of workernodes communicatively coupled to any number and types of data sources.

Thus, embodiments of the DFS system can extend the capabilities of adata intake and query system by leveraging computing assets fromanywhere in a big data ecosystem to collectively execute queries ondiverse data systems regardless of whether data stores are internal ofthe data intake and query system and/or external data stores that arecommunicatively coupled to the data intake and query system over anetwork.

4.1. DFS System Architecture

FIG. 18 is a system diagram illustrating a DFS system architecture inwhich an embodiment may be implemented. The DFS system 200 includes adata intake and query system 202 communicatively coupled to a network ofdistributed components that collectively form a big data ecosystem. Thedata intake and query system 202 may include the components of dataintake and query systems discussed above including any combination offorwarders, indexers, data stores, and a search head. However, the dataintake and query system 202 is illustrated with fewer components to aidin understanding how the disclosed embodiments extend the capabilitiesof data intake and query systems to apply search queries and analyticsoperations on distributed data systems including internal data systems(e.g., indexers with associated data stores) and/or external datasystems in a big data ecosystem.

The data intake and query system 202 includes a search head 210communicatively coupled to multiple peer indexers 206 (also referred toindividually as indexer 206). Each indexer 206 is responsible forstoring and searching a subset of events contained in a correspondingdata store (not shown). The peer indexers 206 can analyze events for asearch query in parallel. For example, each indexer 206 can returnpartial results in response to a search query as applied by the searchhead 210.

The disclosed technique expands the capabilities of the data intake andquery system 202 to obtain and harmonize search results from externaldata sources 209, alone or in combination with the partial searchresults of the indexers 206. More specifically, the data intake andquery system 202 runs various processes to apply a search query to theindexers 206 as well as external data sources 209. For example, a daemon210 of the data intake and query system 202 can operate as a backgroundprocess that coordinates the application of a search query on theindexers and/or the external data stores. As shown, the daemon 210includes software components for the search head 210 and indexers 206 tointerface with a DFS master 212 and a distributed network of workernodes 214, respectively, which are external to the data intake and querysystem 202.

The DFS master 212 is communicatively coupled to the search head 210 viathe daemon 210-3. In some embodiments, the DFS master 212 can includesoftware components running on a device of any system, including thedata intake and query system 202. As such, the DFS master 212 caninclude software and underlying logic for establishing a logicalconnection to the search head 210 when external data systems need to besearched. The DFS master 212 is part of the DFS search service (“searchservice”) that includes a search service provider 216 (also referred toas a query coordinator), which interfaces with the worker nodes 214.

Although shown as separate components, the DFS master 212 and the searchservice provider 216 are components of the search service that mayreside on the same machine, or may be distributed across multiplemachines. In some embodiments, running the DFS master 212 and the searchservice provider 216 on the same machine can increase performance of theDFS system by reducing communications over networks. As such, the searchhead 210 can interact with the search service residing on the samemachine or on different machines. For example, the search head 210 candispatch requests for search queries to the DFS master 212, which canspawn search service providers 216 of the search service for each searchquery.

Other functions of the search service provider 216 can include providingdata isolation across different searches based on role/access control,as well as fault tolerance (e.g., localized to a search head). Forexample, if a search operation fails, then its spawned search serviceprovider may fail but other search service providers for other searchescan continue to operate.

The search head 210 can analyze a query and determine that the DFSsystem 200 can execute the query. Accordingly, the search head 210 cansend the query to the query master 212, which can send it to, or spawn,a search service provider 216. The search service provider can define asearch scheme in response to a received search query that requiressearching both the indexers 206 and the external data sources 209. Aportion of the search scheme can be applied 210 to the indexers 206 andanother portion of the search scheme can be communicated to the workernodes 214 for application to the external data sources 209. The searchservice provider 216 can collect an aggregate of partial search resultsof the indexers 206 and of the external data sources 209 from the workernodes 214, and communicate the aggregate partial search results to thesearch head 210. In some embodiments, the DFS master 212, search head210, or the worker nodes 214 can produce the final search results, whichthe search head 210 can cause to be presented on a user interface of adisplay device.

More specifically, the worker nodes 214 can act as agents of the DFSmaster 212 via the search service provider 216, which can act on behalfof the search head 210 to apply a search query to distributed datasystems. For example, the DFS master 212 can manage different searchoperations and balance workloads in the DFS system 200 by keeping trackof resource utilization while the search service provider 216 isresponsible for executing search operations and obtaining the searchresults.

For example, the search service provider 216 can cause the worker nodes214 to apply a search query to the external data sources 209. The searchservice provider 216 can also cause the worker nodes 214 to collect thepartial search results from the indexers 206 and/or the external datasources 209 over the computer network. Moreover, the search serviceprovider 216 can cause the worker nodes 214 to aggregate the partialsearch results collected from the indexers 206 and/or the external datasources 209.

Hence, the search head 210 can offload at least some processing to theworker nodes 214 because the distributed worker nodes 214 can extractpartial search results from the external data sources 209, and collectthe partial search results of the indexers 206 and the external datasources 209. Moreover, the worker nodes 214 can aggregate the partialsearch results collected from the diverse data systems and transfer themto the search service, which can finalize the search results and sendthem to the search head 210. Aggregating the partial search results ofthe diverse data systems can include combining partial search results,arranging the partial search results in an ordered manner, and/orperforming operations derive other search results from the collectedpartial search results (e.g., transform the partial search results).

Once a logical connection is established between the search head 210,the DFS master 212, the search service provider 216, and the workernodes 214, control and data flows can traverse the components of the DFSsystem 200. For example, the control flow can include instructions fromthe DFS master 212 to the worker nodes 214 to carry out the operationsdetailed further below. Moreover, the data flow can include aggregatepartial search results transferred to the search service provider 216from the worker nodes 214. Further, the partial search results of theindexers 206 can be transferred by peer indexers to the worker nodes 214in accordance with a parallel export technique. A more detaileddescription of the control flow, data flow, and parallel exporttechniques are provided further below.

In some embodiments, the DFS system 200 can use a redistribute operatorof a data intake and query system. The redistribute operator candistribute data in a sharded manner to the different worker nodes 214.Use of the redistribute operator may be more efficient than the parallelexporting because it is closely coupled to the existing data intake andquery system. However, the parallel exporting techniques havecapabilities to interoperate with open source systems other than theworker nodes 214. Hence, use of the redistribute operator can providegreater efficiency but less interoperability and flexibility compared tousing parallel export techniques.

The worker nodes 214 can be communicatively coupled to each other, andto the external data sources 209. Each worker node 214 can include oneor more software components or modules 218 (“modules”) operable to carryout the functions of the DFS system 200 by communicating with the searchservice provider 216, the indexers 206, and the external data sources209. The modules 218 can run on a programming interface of the workernodes 214. An example of such an interface is APACHE SPARK, which is anopen source computing framework that can be used to execute the workernodes 214 with implicit parallelism and fault-tolerance.

In particular, SPARK includes an application programming interface (API)centered on a data structure called a resilient distributed dataset(RDD), which is a read-only multiset of data items distributed over acluster of machines (e.g., the devices running the worker nodes 214).The RDDs function as a working set for distributed programs that offer aform of distributed shared memory.

Thus, the search service provider 216 can act as a manager of the workernodes 214, including their distributed data storage systems, to extract,collect, and store partial search results via their modules 218 runningon a computing framework such as SPARK. However, the embodimentsdisclosed herein are not limited to an implementation that uses SPARK.Instead, any open source or proprietary computing framework running on acomputing device that facilitates iterative, interactive, and/orexploratory data analysis coordinated with other computing devices canbe employed to run the modules 218 for the DFS master 212 to applysearch queries to the distributed data systems.

Accordingly, the worker nodes 214 can harmonize the partial searchresults of a distributed network of data storage systems, and providethose aggregated partial search results to the search service provider216. In some embodiments, the search service provider 216 or DFS master212 can further operate on the aggregated partial search results toobtain final results that are communicated to the search head 210, whichcan output the search results as reports or visualizations on a displaydevice.

The DFS system 200 is scalable to accommodate any number of worker nodes214. As such, the DFS system can scale to accommodate any number ofdistributed data systems upon which a search query can be applied andthe search results can be returned to the search head and presented in aconcise or comprehensive way for an analyst to obtain insights into biddata that is greater in scope and provides deeper insights compared toexisting systems.

4.2. DFS System Operations

FIG. 19 is an operation flow diagram illustrating an example of anoperation flow of the DFS system 200. The operation flow 2100 includescontrol flows and data flows of the data intake and query system 202,the DFS master 212 and/or the search service provider 216 (the DFSmaster 212 and search service provider 216 collectively the “searchservice 220”), one or more worker nodes 214, and/or one or more externaldata sources 209. A combination of the search service 220 and the workernodes 214 collectively enable the data fabric services that can beimplemented on the distributed data systems including, for example, thedata intake and query system 202 and the external data sources 209.

In step 2102, the search head 210 of the data intake and query system202 receives a search query. For example, an analyst may submit a searchquery to the search head 210 over a network from an application (e.g.,web browser) running on a client device, through a network portal (e.g.,website) administered by the data intake and query system 202. Inanother example, the search head 210 may receive the search query inaccordance with a schedule of search queries. The search query can beexpressed in a variety of languages such as a pipeline search language,a structured query language, etc.

In step 2104, the search head 210 processes the search query todetermine whether the DFS system 200 is to handle the search query. Insome embodiments, if the search query only requires searching theindexers 206, the search head 210 can conduct the search on the indexers206 by using, for example, map-reduce techniques without invoking orengaging the DFS system. In some embodiments, however, the search head210 can invoke or engage the DFS system to utilize the worker nodes 214to search the indexers 206 alone, search the external data sources 209alone, or search both and harmonize the partial search results of theindexers 206 alone, and return the search results to the search head 210via the search service 220.

If, search head 210 determines that the DFS system 200 is to handle thesearch query, then the search head 210 can invoke and engage the DFSsystem 200. Accordingly, in some embodiments, the search head 210 canengage the search service 220 when a search query is to be applied to atleast one external data system, such as a combination of the indexers206 and at least one of the external data sources 209, or is otherwiseto be handled by the DFS system 200. 210 The search head 210 can passsearch query to the DFS master 212, which can create (e.g., spawn) asearch service provider (e.g., search service provider 216) to conductthe search.

In some embodiments, the DFS system 200 can be launched by using amodular input, which refers to a platform add-on of the data intake andquery system 202 that can be accessed in a variety of ways such as, forexample, over the Internet on a network portal. For example, the searchhead 210 can use a modular input to launch the search service 220 andworker nodes 214 of the DFS system 200. In some embodiments, a modularinput can be used to launch a monitor function used to monitor nodes ofthe DFS system. In the event that a launched service or node fails, themonitor allows the search head to detect the failed service or node, andre-launch the failed service or node or launch or reuse another launchedservice or node to provide the functions of the failed service or node.In some embodiments, the monitor function for monitoring nodes can belaunched and controlled by the search service provider 216.

In step 2104, the search head 210 executes a search phase generationprocess to define a search scheme based on the scope of the searchquery. The search phase generation process involves an evaluation of thescope of the search query to define one or more phases to be executed bythe data intake and query system 202 and/or the DFS system, to obtainsearch results that would satisfy the search query. The search phases,or layers, may include a combination of phases for initiating searchoperations, searching the indexers 206, searching the external datasources 209, and/or finalizing search results for return back to thesearch head 210.

In some embodiments, the combination of search phases can include phasesfor operating on the partial search results retrieved from the indexers206 and/or the external data sources 209. For example, a search phasemay require correlating or combining partial search results of theindexers 206 and/or the external data sources 209. In some embodiments,a combination of phases may be ordered as a sequence that requires anearlier phase to be completed before a subsequent phase can begin.However, the disclosure is not limited to any combination or order ofsearch phases. Instead, a search scheme can include any number of searchphases arranged in any order that could be different from another searchscheme applied to the same or another arrangement or subset of datasystems.

For example, a first search phase may be executed by the search head 210to extract partial search results from the indexers 206. A second searchphase may be executed by the worker nodes 214 to extract and collectpartial search results from the external data sources 209. A thirdsearch phase may be executed by the indexers 206 and worker nodes 214 toexport partial search results in parallel to the worker nodes 214 fromthe (peer) indexers 206. As such, the third phase involves collectingthe partial search results from the indexers 206 by the worker nodes214. A fourth search phase may be executed by the worker nodes 214 toaggregate (e.g., combine and/or operate on) the partial search resultsof the indexers 206 and/or the worker nodes 214. A sixth and seventhphase may involve transmitting the aggregate partial search results tothe search service 220, and operating on the aggregate partial searchresults to produce final search results, respectively. The searchresults can then be transmitted to the search head 210. In some cases,an eighth search phase may involve further operating on the searchresults by the search head 210 to obtain final search results that canbe, for example, rendered on a user interface of a display device.

In step 2106, the search head 210 initiates a communications searchprotocol that establishes a logical connection with the worker nodes 214via the search service 220. Specifically, the search head 210 maycommunicate information to the search service 220 including a portion ofthe search scheme to be performed by the worker nodes 214. For example,a portion of the search scheme transmitted to the DFS master 212 mayinclude search phase(s) to be performed by the DFS master 212 and theworker nodes 214. The information may also include specific controlinformation enabling the worker nodes 214 to access the indexers 206 aswell as the external data sources 209 subject to the search query.

In step 2108, the search service 220 can define an executable searchprocess performed by the DFS system. For example, the DFS master 212 orthe search service provider 216 can define a search process as a logicaldirected acyclic graph (DAG) based on the search phases included in theportion of the search scheme received from the search head 210.

The DAG includes a finite number of vertices and edges, with each edgedirected from one vertex to another, such that there is no way to startat any vertex and follow a consistently-directed sequence of edges thateventually loops back to the same vertex. Here, the DAG can be adirected graph that defines a topological ordering of the search phasesperformed by the DFS system. As such, a sequence of the verticesrepresents a sequence of search phases such that every edge is directedfrom earlier to later in the sequence of search phases. For example, theDAG may be defined based on a search string for each phase or metadataassociated with a search string. The metadata may be indicative of anordering of the search phases such as, for example, whether results ofany search string depend on results of another search string such thatthe later search string must follow the former search stringsequentially in the DAG.

In step 2110, the search head 210 starts executing local search phasesthat operate on the indexers 206 if the search query requires doing so.If the scope of the search query requires searching at least oneexternal data system, then, in step 2112, the search head 210 sendsinformation to the DFS master 212 triggering execution of the executablesearch process defined in step 2108.

In step 2114, the search service 220 starts executing the search phasesthat cause the worker nodes 214 to extract partial search results fromthe external data stores 209 and collect the extracted partial searchresults at the worker nodes 214, respectively. For example, the searchservice 220 can start executing the search phases of the DAG that causethe worker nodes 214 to search the external data sources 209. Then, instep 2116, the worker nodes 214 collect the partial search resultsextracted from the external data sources 209.

The search phases executed by the DFS system can also cause the workernodes 214 to communicate with the indexers 206. For example, in step2118, the search head 210 can commence a search phase that triggers aremote pipeline executed on the indexers 206 to export their partialsearch results to the worker nodes 214. As such, the worker nodes 214can collect the partial search results of the indexers 206. However, ifthe search query does not require searching the indexers 206, then thesearch head 210 may bypass triggering the pipeline of partial searchresults from the indexers 206.

In step 2122, the worker nodes 214 can aggregate the partial searchresults and send them to the search service 220. For example, the searchservice provider 216 can begin collecting the aggregated search resultsfrom the worker nodes 214. The aggregation of the partial search resultsmay include combining the partial search results of indexers 206, theexternal data stores 209, or both. In some embodiments, the aggregatedpartial search results can be time-ordered or unordered depending on therequirements of the type of search query.

In some embodiments, aggregation of the partial search results mayinvolve performing one or more operations on a combination of partialsearch results. For example, the worker nodes 214 may operate on acombination of partial search results with an operator to output a valuederived from the combination of partial search results. Thistransformation may be required by the search query. For example, thesearch query may be an average or count of data events that includespecific keywords. In another example, the transformation may involvedetermining a correlation among data from different data sources thathave a common keyword. As such, transforming the search results mayinvolve creating new data derived from the partial search resultsobtained from the indexers 206 and/or external data systems 209.

In step 2124, a data pipeline is formed to the search head 210 throughthe search service 220 once the worker nodes 214 have received thepartial search results from the indexers 206 and the external datastores 209, and aggregated the partial search results (e.g., andtransformed the partial search results).

In step 2126, the aggregate search received by the search service 220may optionally be operated on to produce final search results. Forexample, the aggregate search results may include different statisticalvalues of partial search results collected from different worker nodes214. The search service 220 may operate on those statistical values toproduce search results that reflect statistical values of thestatistical values obtained from the all the worker nodes 214.

As such, the produced search results can be transferred in a big datapipeline to the search head 210. The big data pipeline is essentially apipeline of the data intake and query system 202 extended into the bigdata ecosystem. Hence, the search results are transmitting to the searchhead 210 where the search query was received by a user. Lastly, in step2128, the search head 210 can render the search results or dataindicative of the search results on a display device. For example, thesearch head 210 can make the search results available for visualizing ona user interface rendered via a computer portal.

It will be understood that fewer or more steps can be included in theoperation flow 2100. Further, some operations can be performed bydifferent components of the system. In some embodiments, for example,some of the tasks described as being performed by the search head 210can be performed by the search service 220, such as the search serviceprovider 216. As a non-limiting example, step 2104 can be omitted andsteps 2110, 2112, and 2118 can be performed by the search serviceprovider 216. For example, upon receiving the search query at step 2102,the search head 210 can determine that the DFS system 200 will handlethe query. Accordingly, at 2106, the search head can communicate thesearch query to the search service 220 to initiate the search. In turn,the search service provider 216 can define the search scheme 2104 andsearch process 2108. As part of defining the search scheme and process2108, the search service provider 216 can determine whether any indexers206 or external data sources 209 will be accessed. Once the scheme andprocess are defined, the search service provider 216 can trigger asearch of the indexers (2110) and an external search of the externaldata sources (2112). The partial search results from both can becommunicated to the worker nodes 214 for processing (2116, 2118), whichcan aggregate them together (2122). The results can then be provided tothe search service 220 (2124), further processed (2126), and thencommunicated to the search head 210 for rendering for the client device(2128). In some cases, the further processing 2126 performed by thesearch service 220 can include additional transforms on the resultsreceived from the worker nodes 214 based on the query. Accordingly, insuch an embodiment, the system can delegate some of the search head 210processing to the search service 220, thereby freeing up the search head210 to handle additional queries.

5.0. Parallel Export Techniques

The disclosed embodiments include techniques for exporting partialsearch results in parallel from peer indexers of a data intake and querysystem to the worker nodes. In particular, partial search results (e.g.,time-indexed events) obtained from peer indexers can be exported inparallel from the peer indexers to worker nodes. Exporting the partialsearch results from the peer indexers in parallel can improve the rateat which the partial search results are transferred to the worker nodesfor subsequent combination with partial search results of the externaldata systems. As such, the rate at which the search results of a searchquery can be obtained from the distributed data system can be improvedby implementing parallel export techniques.

FIG. 20 is an operation flow diagram illustrating an example of aparallel export operation performed in a DFS system according to someembodiments of the present disclosure. The operation 2200 for parallelexporting of partial search results from peer indexers 206 begins byprocessing a search query that requires transferring of partial searchresults from the peer indexers 206 to the worker nodes 214.

In step 2202, the search head 210 receives a search query as, forexample, input by a user of a client device. In step 2210, the searchhead 210 processes the search query to determine whether internal datastores 222 of peer indexers 206 must be searched for partial searchresults. If so, in step 2206, the search head 210 executes a process tosearch the peer indexers 206 and retrieve the partial search results. Instep 2209, each peer indexer 206 can return its partial search resultsretrieved from respective internal data stores 222.

In step 2210, the partial search results (e.g., time-indexed events)obtained by the peer indexers 206 can be sharded into chunks of events(“event chunks”). Sharding involves partitioning large data sets intosmaller, faster, more easily managed parts called data shards. Thesharded partitions can be determined from policies, which can be basedon hash values by default. Accordingly, the retrieved events can begrouped into chunks (i.e., micro-batches) based on a value associatedwith a search query and/or the corresponding retrieved events. Forexample, the retrieved events can be sharded in chunks based on thefield names passed as part of a search query process of the data intakeand query system. The event chunks can then be exported from the peerindexers 206 in parallel over the network to the worker nodes 214.

If time-ordering is required, the parallel exporting technique caninclude a mechanism to reconstruct the ordering of event chunks at theworker nodes 214. In particular, the order from which the event chunksflowed from peer indexers 206 can be tracked to enable collating thechunks in time order at the worker nodes 214. For example, metadata ofevent chunks can be preserved when parallel exporting such that thechunks can be collated by the worker nodes 214 that receive the eventchunks. Examples of the metadata include SearchResultsInfo (SRI) (a datastructure of SPLUNK® which carries control and meta information for thesearch operations) or timestamps indicative of, for example, the timeswhen respective events or event chunks started flowing out from the peerindexers 206. If time ordering is not required, preserving the timeordering of chunks by using timestamps may be unnecessary.

The parallel exporting technique can be modified in a variety of ways toimprove performance of the DFS system. For example, in step 2214, theevent chunks can be load balanced across the peer indexers 206 and/orreceiving worker nodes 214 to improve network efficiency and utilizationof network resources. In particular, a dynamic list of receivers (e.g.,worker nodes 214) can be maintained by software running on hardwareimplementing the DFS system. The list may indicate a currentavailability of worker nodes to receive chunks from export processors ofthe peer indexers 206. The list can be updated dynamically to reflectthe availability of the worker nodes 214. Further, parameters on thelist indicative of the availability of the worker nodes 214 can bepassed to the export processers periodically or upon the occurrence ofan event (e.g., a worker node 214 becomes available). The exportprocessers can then perform a load balancing operation on the eventchunks over the receiving worker nodes 214.

The worker nodes 214 may include driver programs that consume the eventsand event chunks. In some embodiments, the worker nodes 214 can includea software development kit (SDK) that allows third party developers tocontrol the consumption of events from the peer indexers 206 by theworker nodes 214. As such, third party developers can control thedrivers causing the consumption of events and event chunks from the peerindexers 206 by the worker nodes 214. Lastly, in step 2216, the eventchunks are exported from the peer indexers 206 in parallel to the workernodes 214.

In some embodiments, the rate of exporting events or event chunks inparallel by the peer indexers 206 can be based on an amount of sharedmemory available to the worker nodes 214. Accordingly, techniques can beemployed to reduce the amount of memory required to store transferredevents. For example, when the worker nodes 214 are not local (e.g.,remote from the peer indexers 206), compressed payloads of the eventchunks can be transferred to improve performance.

Thus, the disclosed DFS system can provide a big data pipeline andnative processor as a mechanism to execute infrastructure, analytics,and domain-based processors based on data from one or more external datasources over different compute engines. In addition, the mechanism canexecute parallelized queries to extract results from external systems.

It will be understood that fewer or more steps can be included in theoperation flow 2100. Further, some operations can be performed bydifferent components of the system. In some embodiments, for example,some of the tasks described as being performed by the search head 210can be performed by the search service 220, such as the search serviceprovider 216.

As a non-limiting example, the search head 210 can process the searchquery to determine whether the search query is to be handled by the DFSsystem 202. For example, in some embodiments, the search head 210 canhandle queries for the indexers 206 and in other embodiments, the searchprocess 220 can handle queries for the indexers 206. Based on adetermination that the search process is to handle the search query, thesearch head 210 can forward the query to the search process 220. Thesearch service provider 216 can further process the query (2210) anddetermine that the search includes searching the indexer 206. As such,the search service provider can execute a process to search the peerindexers 206 and provide the partial search results to the worker nodes214, or instruct the worker nodes 214 to instruct the indexers 206 toexecute the search. Steps 2210, 2212, 2214, 2216, and 2218 can thenperform as illustrated such that the partial search results are exportedto the worker nodes 214 for further processing.

6.0. DFS Query Processing

The disclosed embodiments include techniques to process search queriesin different ways by the DFS system depending on the type of searchresults sought in response to a search query. In other words, a dataintake and query system can receive search queries that cause the DFSsystem to process the search queries differently based on the searchresults sought in accordance with the search queries. For example, somesearch queries may require ordered search results, and an order of thesearch results may be unimportant for other search queries.

To obtain ordered search results, a search query executed on internaldata sources (e.g., indexers) and/or external data sources may requiresorting and organizing timestamped partial search results across themultiple diverse data sources. However, the multiple internal orexternal data sources may not store timestamped data. That is, some datasources may store time-ordered data while other data sources may notstore time-ordered data, which prevents returning time-ordered searchresults for a search query. The disclosed embodiments provide techniquesfor harmonizing time-ordered and unordered data from across multipleinternal or external data sources to provide time-ordered searchresults.

In other instances, a search query may require search results thatinvolve performing a transformation of data collected from multipleinternal and/or external data sources. The transformed data can beprovided as the search results in response to the search query. In somecases, the search query may be agnostic to the ordering of the searchresults. For example, the search results of a search query may requirecounts of different types of events generated over the same period oftime. Hence, search results that satisfy the search query could beordered or unordered counts. As such, there is no requirement tomaintain the time order of the partial search results obtained from datasystems subject to the count search query. Thus, the techniquesdescribed below provide mechanisms to obtain search result from the biddata ecosystem that are transformed, time-ordered, unordered, or anycombinations of these types of search results.

6.1. Ordered Search Results

The disclosed embodiments include techniques to obtain ordered searchresults based on partial search results from across multiple diverseinternal and/or external data sources. The ordering of the searchresults may be with respect to a parameter associated with the partialsearch results. An example of a parameter includes time. As such, thedisclosed technique can provide a time-ordered search result based onpartial search results obtained from across multiple internal and/orexternal data sources. Moreover, the disclosed technique can providetime-ordered search results regardless of whether the partial searchresults obtained from the diverse data sources are timestamped.

An ordered search (i.e., ordered data execution) can be referred to as“cursored” mode of data access. According to this mode of data access,the DFS system can execute time-ordered searches or retrieve events frommultiple data sources and presents the events in a time ordered manner.For searches involving only local data sources, the DFS system canimplement a micro-batching mechanism based on the event time acrossworker nodes. The DFS system can ensure that per peer ordering isenforced across the worker nodes and final collation is performed at alocal search head or search service provider. In case of event retrievalfrom multiple data sources, the DFS system can maintain per sourceordering prior to ordered collation in the local search head or searchservice provider.

FIG. 21 is a flowchart illustrating a method performed the DFS system toobtain time-ordered search results in response to a cursored searchquery according to some embodiments of the present disclosure. Asdescribed below, the method 2300 for processing cursored search queriescan involve a micro-batching process executed by worker nodes to ensuretime orderliness of partial search results obtained from data sources.

In step 2302, one or more worker nodes collect partial search resultsfrom the internal and/or external data sources. For example, the workernode may collect partial search results corresponding to data having adata structure as specified by the search query. In another example, theworker nodes may query an external data source for partial searchresults based on specific keywords specified by a cursored search query,and collect the partial search results. The worker nodes may alsocollect partial search results from indexers, which were returned inresponse to application of the search query by the search head (orsearch service provider) to the indexers. In some embodiments, thepartial search results may be communicated from each data source to theworker node in chunks (i.e., micro-batches).

In step 2304, the worker nodes perform deserialization of the partialsearch results obtained from the data sources. Specifically, partialsearch results transmitted by the data sources could been serializedsuch that data objects were converted into a stream of bytes in order totransmit the object, or store the object in memory. The serializationprocess allows for saving the state of an object in order to reconstructit at the worker node by using reverse process of deserialization.

In step 2306, the worker nodes receive the partial search resultscollected from the data sources and transform them into a specifiedformat. As such, partial search results in diverse formats can betransformed into a common specified format. The specified format may bespecified to facilitate processing by the worker nodes. Hence, diversedata types obtained from diverse data sources can be transformed into acommon format to facilitate subsequent aggregation across all thepartial search results obtained in response to the search query. As aresult, the partial search results obtained by the worker nodes can betransformed into, for example, data events having structures that arecompatible to the data intake and query system.

In step 2308, the worker nodes may determine whether the partial searchresults are associated with respective time values. For example, theworker nodes may determine that events or event chunks from an internaldata source are timestamped as shown in FIG. 2 , but events or eventchunks from an external data source may not be timestamped. Thetimestamped events may also be marked with an “OriginType” (e.g.,mysql-origin, cloud-aws-s), “SourceType” (e.g., cvs, json, sql), and“Host < >” (e.g., IP address where the event originated), or other datauseful for ordering the partial search. If all the partial searchresults from across the diverse data systems are adequately marked, thenharmonizing the partial search results may not require different typesof processing. However, typically at least some partial search resultsfrom across the diverse distributed data systems are not adequatelymarked to facilitate harmonization.

Accordingly, the worker nodes can implement bifurcate processing of thepartial search results depending on whether or not the partial searchresults are adequately marked. Specifically, the partial search resultsthat are timestamped can be processed one way, and the partial searchresults that are not timestamped can be processed a different way. Theworker nodes can execute the different types of processinginterchangeably, or execute one type of processing after the other typeof processing has completed.

In step 2310, for time-ordered partial search results, respective workernodes can be assigned (e.g., fixed) to receive time-ordered partialsearch results (e.g., events or event chunks) from respective datasources in an effort to maintain the time orderliness of the data.Assigning a worker node to obtain time-ordered partial search results ofthe same data source avoids the need for additional processing amongmultiple nodes otherwise required if they each received differenttime-ordered chunks from the same data source. In other words, setting aworker node to collect all the time-ordered partial search results fromits source avoids the added need to distribute the time-ordered partialsearch results between worker nodes to reconstruct the overall timeorderliness of the partial search results.

For example, a worker node can respond to timestamped partial searchresults it receives by setting itself (or another worker node) toreceive all of the partial search results from the source of thetime-stamped partial search results. For example, the worker node can beset by broadcasting the assignment to other worker nodes, whichcollectively maintain a list of assigned worker nodes and data sources.In some embodiments, a worker node that receives timestamped partialsearch results can communicate an indication about the timestampedpartial search results to the DFS master or search service provider.Then the DFS master or search service provider can set a specific set ofworker nodes to receive all the timestamped data from the specificsource.

In step 2312, the worker nodes read the collected partial search results(e.g., events or event chunks) and arrange the partial search results intime order. For example, each collected event or event chunk may beassociated with any combination of a start time, an end time, a creationtime, or some other time value. The worker node can use the time values(e.g., timestamps) associated with the events or event chunks to arrangethe events and/or the event chunks in a time-order. Lastly, in step2314, the worker nodes may stream the time-ordered partial searchresults in parallel as time-ordered chunks via the search service (e.g.,to the DFS master or search service provider of the DFS system).

Referring back to step 2308, the worker nodes can respond differently topartials search results that are not associated with timestamps (e.g.,lack an associated time value that facilitates time ordering). In step2316, the worker nodes can associate events or chunks with a time valueindicative of the time of ingestion of the events or event chunks by therespective worker nodes (e.g., an ingestion timestamp). The worker nodescan associate the partial search results with any time value that can bemeasured relative to a reference time value (i.e., not limited to aningestion timestamp). In some embodiments, the partial search resultstimestamped by the worker nodes can also be marked with a flag todistinguish those partial search results from the partial search resultsthat were timestamped before being collected by the worker nodes.

In step 2318, the worker nodes sort the newly timestamped partial searchresults and create chunks (e.g., micro-batches) upon completion ofcollecting all of the partial search results from the data sources. Insome embodiments, the chunks may be created to contain a default minimumor maximum number of partial search results (i.e., a default chunksize). As such, the worker nodes can create time-ordered partial searchresults obtained from data sources that did not provide time-orderedpartial search results.

In step 2320, the worker nodes can apply spillover techniques to disk asneeded. In some embodiments, the worker nodes can provide an extensiveHB/status update mechanism to notify the DFS master of its currentblocked state. In some embodiments, the worker nodes can ensure akeep-alive to override timeout and provide notifications. Lastly, instep 2322, the worker nodes may stream the time-ordered partial searchresults in parallel as time-ordered chunks via the search service (e.g.,to the DFS master or search service provider of the DFS system).

Accordingly, time-ordered partial search results can be created from acombination of time-ordered and non-time-ordered partial searchcollected from diverse data sources. The time-ordered partial searchresults can be streamed in parallel from multiple worker nodes to theservice provider, which can stream each search stream to the search headof the data intake and query system. As such, time-ordered searchresults can be produced from diverse data types of diverse data systemswhen the scope of a search query requires doing so.

FIG. 22 is a flowchart illustrating a method performed by a data intakeand query system of a DFS system in response to a cursored search queryaccording to some embodiments of the present disclosure. Specifically,the method 2400 can be performed by the data intake and query system tocollate the time-ordered partial search results obtained by queryinginternal and/or external data sources.

In step 2402, the search head, search service provider, or one or moreworker nodes receive one or more streams of time-ordered partial searchresults (e.g., event chunks) from a data source. In step 2404, thesearch head or search service provider creates multiple searchcollectors to collect the time ordered event chunks.

For example, the search head or search service provider can add a classof collectors to collate search results from the worker nodes. In someembodiments, the search head or search service provider can createmultiple collectors; such as a collector for each indexer, as well as asingle collector for each external data source or other data source. Insome embodiments, the search head or search service provider may createa collector for each stream, which could include time-ordered chunksfrom a single worker node or a single data source. Hence, each collectorreceives time-ordered chunks.

In step 2406, the collectors perform a deserialization process on thereceived chunks and their contents, which had been serialized fortransmission from the search service. In step 2408, each collector addsthe de-serialized partial search or their chunks to a collector queue.The search head or search service provider may include any number ofcollector queues. For example, the search head or search serviceprovider may include a collector queue for each collector or for eachdata source that provided partial search results.

In step 2410, the search head, search service provider, or designatedworker node(s) can collate the time-ordered partial search resultsobtained from the data sources as time-ordered search results of thepresented search query. For example, the search head, search serviceprovider, or designated worker node(s) may apply a collation operationbased on the time-order of events contained in the chunks from thequeues of different collectors to provide time-ordered search results.

Lastly, in step 2412, the time-ordered search results could be providedto an analyst on a variety of mediums and in a variety of formats. Forexample, the time-ordered search results may be rendered as a timelinevisualization on a user interface on a display device. In someembodiments, the raw search results (e.g., entire raw events) areprovided for the timeline visualization.

The visualization can allow the analyst to investigate the searchresults. In another example, the time-ordered results may be provided toan analyst automatically on printed reports, or transmitted in a messagesent over a network to a device to alert the analyst of a conditionbased on the search results.

Although the methods illustrated in FIGS. 23 and 24 include acombination of steps to obtain time-ordered search results from acrossdiverse data sources that may or may not provide timestamped data, thedisclosed embodiments are not so limited. Instead, any portion of thecombination of steps illustrated in FIGS. 23 and 24 could be performeddepending on the scope of the search query. For example, only a subsetof steps may be performed when the search results for a search query areobtained exclusively from a single external data source that storestimestamped data.

6.2. Transformed Search Results

The disclosed embodiments include a technique to obtain search resultsfrom the application of transformation operations on partial searchresults obtained from across internal and/or external data sources.Examples of transformation operations include arithmetic operations suchas an average, mean, count, or the like. Examples of reportingtransformations include join operations, statistics, sort, top head.Hence, the search results of a search query can be derived from partialsearch results rather than include the actual partial search results. Inthis case, the ordering of the search results may be nonessential. Anexample of a search query that requires a transformation operation is a“batch” or “reporting” search query. The related disclosed techniquesinvolve obtaining data stored in the bid data ecosystem, and returningthat data or data derived from that data.

According to a reporting or batch mode of data access, the DFS systemexecutes blocking transforming searches, for example, to join across oneor multiple available data sources. Since ordering is not needed, theDFS system can implement sharding of the data from the various datasources and execute aggregation (e.g., reduction of map-reduction) inparallel. The DFS architecture can also execute multiple DFS operationsin parallel to receive sharded data from the different sources.

FIG. 23 is a flowchart illustrating a method performed by nodes of a DFSsystem to obtain search results in response to a batch or reportingsearch query according to some embodiments of the present disclosure.The method 2500 for processing batch or reporting search queries caninvolve steps performed by the DFS master, the service provider, and/orworker nodes to transform partial search results into search resultsinto batch or reporting search results. The disclosed techniques alsosupport both streaming and non-streaming for multiple data sources.

The transformation operations generally occur at the worker nodes. Forexample, an operation may include a statistical count of events having aparticular IP address. The DFS can shard the data in certain partitions,and then each worker node can apply the transformation to thatparticular partition. In case it is the last reporting/transformingprocessor, then the transformed results are collated at the searchservice provider, and then transmitted to the search head. However, ifthere is a reporting search beyond the statistical count, then anotherreshuffle of the partial search results can be executed among the workernodes to put the different partitions on the same worker node, and thentransforms can be applied. If this is the last reporting search, thenresults are sent back to the service provide node and then to the searchhead. This process continues as dictated by the DAG generated from thephase desired by the search head.

In step 2502, the worker nodes collect partial search results from theinternal and/or external data sources. For example, a worker node maycollect partial search results including data having data structuresspecified by the search query. In another example, the worker node mayquery an external data source for partial search results based onspecific keywords included in a reporting search query, and collect thepartial search results. The worker node may also collect partial searchresults from indexers, which were returned in response to application ofthe reporting search query by the search head (or search serviceprovider or nodes) to the indexers. The partial search results may becommunicated from each data source to the worker nodes individually orin chunks (i.e., micro-batches). The worker nodes thus ingest partialsearch results obtained from the data sources in response to a searchquery.

In step 2504, the worker nodes can perform deserialization of thepartial search results obtained from the data sources. Specifically, thepartial search results transmitted by the data sources can be serializedby converting objects into a stream of bytes, which allows for savingthe state of an object for subsequent recreation of the object at theworker nodes by using the reverse process of deserialization.

In step 2506, the worker nodes transform the de-serialized partialsearch results into a specified format. As such, partial search resultscollected in diverse formats can be transformed into a common specifiedformat. The specified format may be specified to facilitate processingby a worker node. As such, diverse data types obtained from diverse datasources can be transformed into a common format to facilitate subsequentaggregation across all the partial search results obtained in responseto the search query. As a result, the partial search results obtained byworker nodes can be transformed into, for example, data events havingstructures that are compatible to the data intake and query system.

Unlike cursored search queries, the time-order of partial search resultsis not necessarily considered when processing reporting queries.However, in step 2508, if a data source returns partial search resultsthat are not associated with time values (e.g., no timestamp), theworker nodes can associate events or event chunks with a time valueindicative of the time of ingestion of the events or chunks by theworker nodes (e.g., ingestion timestamp). In some embodiments, theworker nodes can associate the partial search results with any timevalue that can be measured relative to a reference time value.Associating time values with partial search results may facilitatetracking partial search results when processing reporting searches, ormay be necessary when performing reporting searches that requiretime-ordered results (e.g., a hybrid of cursored and reportingsearches).

In step 2510, the worker nodes determine whether the ingested partialsearch results were obtained by an internal data source or an externaldata source to bifurcate processing respectively. In other words, theworker nodes process the ingested partial search results differentlydepending on whether they were obtained from an internal data source(e.g., indexers) or an external data source, if needed. That is, thiscan be the case only when reporting searches are run in the indexers;however, if all the processors in the indexers are streaming, then noprocessing unique to the indexer data is needed. However, data fromexternal data sources can be sanitized in terms of coding, timestamped,and throttles based on the timestamp.

In step 2512, for internal data sources, the worker nodes read thepartial search results obtained from indexers of a data intake and querysystem in a sharded way. In particular, the worker nodes may use a listidentifying indexers from which to pull the sharded partial searchresults. As discussed above, sharding involves partitioning datasetsinto smaller, faster, and more manageable parts called data shards. Thesharded partitions can be determined from policies, which can be basedon hash values by default. In the context of map-reduce techniques, themap step can be determined by the sharding and a predicate passed, whichmaps records matching the predicate to whatever is needed as the searchresult. The reduce step involves the aggregation of the shards. Theresults of a query are those items for which the predicate returns true.

In step 2514, the partial search results of the indexers are aggregated(e.g., combined and/or transformed) by the worker nodes. In particular,the partial search results can be in a pre-streaming format(semi-reduced), and need to be aggregated (e.g., reduced or combined)prior to aggregation with partial search results of external datasources. In step 2516, the aggregated partial search results of theindexers are aggregated (e.g., combined and/or transformed) with thepartial search results obtained from external data sources. Lastly, instep 2518, the aggregated partial search results of internal andexternal data stores can be transmitted from the worker nodes inparallel to the search service (e.g., to the DFS master or searchservice provider of the DFS system).

In step 2520, for external data sources, the worker nodes pushpredicates for the reporting search query to the external data sources.A predicate is a function that takes an argument, and returns a Booleanvalue indicating of true or false. The predicate can be passed as aquery expression including candidate items, which can be evaluated toreturn a true or false value for each candidate item.

In step 2522, the network nodes can determine whether the external datasources may or may not be able to execute a sharded query. In step 2526,for an external data source that can execute a sharded query, the workernode reads the results in different shards. In some embodiments, the DFSmaster randomly chooses which worker nodes will execute the shards. Instep 2524, for an external data sources that cannot execute a shardedquery, a worker node has the ability to spillover to disk, andredistribute to other worker nodes.

In step 2528, the worker nodes can apply an aggregation (e.g., (e.g.,combine and/or transform) or stream processing to have the partialsearch results ready for further processing against results from partialsearch results from the internal sources. Thus, referring back to step2516, the worker nodes aggregate the partial search results from alldata sources in response in response to the search query. For example,the worker nodes can apply a process similar to a reduction step of amap-reduce operation across all the partial search results obtained fromdiverse data sources. Then, in step 2518, the aggregate partial searchresults can be transmitted from the worker nodes in parallel to thesearch service provider 216. In particular, the search service provider,can collect all the finalized searches results from the worker nodes,and return the results to the search head.

FIG. 24 is a flowchart illustrating a method performed by a data intakeand query system of a DFS system in response to a batch or reportingsearch query according to some embodiments of the present disclosure. Inparticular, the method 2600 is performed by the data intake and querysystem to provide the batch or reporting search results obtained byquerying internal and/or external data sources.

In step 2602, a search head, search service provider, or designatedworker node(s) of receives the aggregate partial search results via ahybrid collector. The number and function of the hybrid collectors isdefined depending on the type of search executed. For example, for thetransforming search, the search head or search service provider cancreate only one collector to receive the final results from the workernodes and after serialization directly pushes into the search resultqueue. In step 2604, the search head or search service provider uses anexisting job pool to de-serialize search results, and can push thesearch results out. In such an operation, collation is not needed.

Lastly, in step 2606, the transformed search results could be providedto an analyst on a variety of mediums and in a variety of formats. Forexample, the time-ordered search results may be rendered as a timelinevisualization on a user interface on a display device. The visualizationcan allow the analyst to investigate the search results. In anotherexample, the time-ordered results may be provided to an analystautomatically on printed reports, or transmitted in a message sent overa network to a device to alert the analyst of a condition based on thesearch results.

Although the methods illustrated in FIGS. 23 through 26 include acombination of steps to obtain time ordered, unordered, or transformedsearch results from across multiple data sources that may or may notstore timestamped data, the disclosed embodiments are not so limited.Instead, a portion of a combination of steps illustrated in any of thesefigures could be performed depending on the scope of the search query.For example, only a subset of steps may be performed when the partialsearch results for a search query is obtained exclusively from anexternal data source.

7.0. Co-Located Deployment Architecture

The capabilities of a data intake and query system can be improved byimplementing the DFS system described above in a co-located deploymentwith the data intake and query system. For example, FIG. 25 is a systemdiagram illustrating a co-located deployment of a DFS system with thedata intake and query system in which an embodiment may be implemented.

In the illustrated embodiment, the system 224 shows only some componentsof a data intake and query system but can include other components(e.g., forwarders, internal data stores) that have been omitted forbrevity. In particular, the system 224 includes search heads 226-1 and226-2 (referred to collectively as search heads 226). The search heads226 collectively form a search head cluster 228. Although shown withonly two search heads, the cluster 228 can include any number of searchheads. Alternatively, an embodiment of the co-located deployment caninclude a single search head rather than the cluster 228.

The search heads 226 can operate alone or collectively to carry outsearch operations in the context of the co-located deployment. Forexample, a search head of the cluster 228 can operate as a leader thatorchestrates search. As shown, the search head 226-1 is a leader of thecluster 228. Any of the search heads 226 can receive search queries thatare processed collectively by the cluster 228. In some embodiments, aparticular search head can be designated to receive a search query andcoordinate the operations of some or all of the search heads of acluster 228. In some embodiments, a search head of the cluster 228 cansupport failover operations in the event that another search head of thecluster 228 fails.

The cluster 228 is coupled to N peer indexers 230. In particular, thesearch head 226-1 can be a leader of the cluster 228 that is coupled toeach of the N peer indexers 230. The system 224 can run one or moredaemons 232 that can carry out the DFS operations of the co-locateddeployment. In particular, the daemon 232-1 of the search head 226-1 iscommunicatively coupled to a DFS master 234, which coordinates controlof DFS operations. Moreover, each of the N peer indexers 230 run daemons232 communicatively coupled to respective worker nodes 236. The workernodes 236 are coupled to one or more data sources from which data can becollected as the partial search results of a search query. For example,the worker nodes 236 can collect partial search results of the indexersfrom internal data sources (not shown) and one or more of external datasources 240. Lastly, the worker nodes 236 are communicatively coupled tothe DFS master 234 or a search service provider to form the DFSarchitecture of the illustrated co-located embodiment.

7.1. Co-Located Deployment Operations

FIG. 26 is an operation flow diagram illustrating an example of anoperation flow of a co-located deployment of a DFS system with a dataintake and query system according to some embodiments of the presentdisclosure. The operational flow 2800 shows the processes forestablishing the co-located DFS system and search operations carried outin the context of the co-located deployment.

In step 2802, a search head of the cluster 228 can launch the DFS master234 and/or launch a connection to the DFS master 234. For example, asearch head can use a modular input to launch an open source DFS master234. Moreover, the search head can use the modular input to launch amonitor of the DFS master 234. The modular input can be a platformadd-on of the data intake and query system that can be accessed in avariety of ways such as, for example, over the Internet on a networkportal.

In step 2804, the peer indexers 230 can launch worker nodes 236. Forexample, each peer indexer 230 can use a modular input to launch an opensource worker node. In some embodiments, only some of the peer indexers230 launch worker nodes, which results in a topology where not all ofthe peer indexers 230 have an associated worker node. Moreover, the peerindexers 206 can use the modular input to launch a monitor of the workernodes 236.

In step 2806, the cluster 228 can launch one or more instances of a DFSservice. For example, any or each of the search heads of the cluster 228can launch or communicate with an instance of the DFS service. Hence,the co-located deployment can launch and use multiple instances of a DFSservice but need only launch and use a single DFS master 234. In theevent that a launched DFS master fails, the lead search head using themonitoring modular input can restart the failed DFS master. However, ifthe DFS master fails along with the lead search head, another searchhead can be designated as the cluster 228's leader and can re-launch theDFS master.

In step 2808, a search head of the cluster 228 can receive a searchquery. For example, a search query may be input by a user on a userinterface of a display device. In another example, the search query canbe input to the search head in accordance with a scheduled search.

In step 2810, a search head of the cluster 228 can initiate a DFS searchsession with the local DFS service. For example, any of the membersearch heads of the cluster 228 can receive a search query and, inresponse to the search query, a search head can initiate a DFS searchsession using an instance of the DFS service.

In step 2812, a search head of the cluster 228 (or a search serviceprovider) triggers a distributed search on the peer indexers 230 if thesearch query requires doing so. In other words, the search query isapplied on the peer indexers 230 to collect partial search results frominternal data stores (not shown).

In step 2814, the distributed search operations continue with the peerindexers 230 retrieving partial search results from internal datastores, and transporting those partial search results to the workernodes 236. In some embodiments, the internal partial search results arepartially reduced (e.g., combined), and transported by the peer indexers230 to their respective worker nodes 236 in accordance with parallelexporting techniques. In some embodiments, if each peer indexer does nothave an associated worker node, the peer indexer can transfer itspartial search results to the nearest worker node in the topology ofworker nodes. In step 2816, the worker nodes 236 collect the partialsearch results extracted from the external data sources 240.

In step 2818, the worker nodes 236 can aggregate (e.g., merge andreduce) the partial search results from the internal data sources andthe external data sources 240. For example, the aggregation of thepartial search results may include combining the partial search resultsof indexers 230 and/or the external data stores 240. Hence, the workernodes 236 can aggregate the collective partial search results at scalebased on DFS native processors residing at the worker nodes 236.

In some embodiments, the aggregated partial search results can be storedin memory at worker nodes before being transferred between other workernodes to execute a multi-staged parallel aggregation operation. Onceaggregation of the partial search results has been completed (e.g.,completely reduced) at the worker node 236, the aggregated partialsearch results can be read by the DFS service running locally to thecluster 228. For example, the DFS service can commence reading theaggregated search results as event chunks.

In step 2820, the aggregate partial search results read by the DFSservice are transferred to the DFS master 234 or search serviceprovider. Then, in step 2822, the DFS master 234 can transfer the finalsearch results to the cluster 228. For example, the aggregated partialsearch results can be transferred by the worker nodes 236 as eventchunks at scale to the DFS master 234, which can transfer search results(e.g., those received or derived therefrom) to the lead search headorchestrating the DFS session.

Lastly, in step 2822, a search head can cause the search results or dataindicative of the search results to be rendered on user interface of adisplay device. For example, the search head member can make the searchresults available for visualizing on a user interface rendered on thedisplay device.

It will be understood that fewer or more, or different steps can beincluded in the operation flow 2800. Further, some operations can beperformed by different components of the system. In some embodiments,for example, some of the tasks described as being performed by thesearch head 210 can be performed by the search service 220, such as thesearch service provider 216. In some cases, step 2806 can be omitted. Insome cases, upon determining that a search query is to be handled by thesearch service, the cluster 228 can communicate the query to the searchservice. In turn, the search service can trigger the distributed search,etc.

8.0. Cloud Deployment Architecture

The performance and flexibility of a data intake and query system havingcapabilities extended by a DFS system can be improved with deployment ona cloud computing platform. For example, FIG. 27 is a cloud-based systemdiagram illustrating a cloud deployment of a DFS system in which anembodiment may be implemented.

In particular, a cloud computing platform can share processing resourcesand data in a multi-tenant network. As such, the platform's computingservices can be used on demand in a cloud deployment of a DFS system.The platform's ubiquitous, on-demand access to a shared pool ofconfigurable computing resources (e.g., networks, servers, storage,applications, and services), which can be rapidly provisioned andreleased with minimal effort, can be used to improve the performance andflexibility of a data intake and query system extended by a DFS system.

In the illustrated embodiment, a cloud-based system 242 includescomponents of a data intake and query system extended by the DFS systemimplemented on a cloud computing platform. However, the cloud-basedsystem 242 is shown with only some components of a data intake and querysystem in a cloud deployment but can include other components (e.g.,forwarders) that have been omitted for brevity. As such, the componentsof the cloud-based system 242 can be understood by analogy to otherembodiments described elsewhere in this disclosure.

An example of a suitable cloud computing platform include Amazon webservices (AWS), which includes elastic MapReduce (EMR) web services.However, the disclosed embodiments are not so limited. Instead, thecloud-based system 242 could include any cloud computing platform thatuses EMR-like clusters (“EMR clusters”).

In particular, the cloud-based system 242 includes a search head 244 asa tenant of a cloud computing platform. Although shown with only thesearch head 244, the cloud-based system 242 can include any number ofsearch heads that act independently or collectively in a cluster. Thesearch head 244 and other components of the cloud-based system 242 canbe configured on the cloud computing platform.

The cloud-based system 242 also includes any number of worker nodes 246as cloud instances (“cloud worker nodes 246”). The cloud worker nodes246 can include software modules 248 running on hardware devices of acloud computing platform. The software modules 248 of the cloud workernodes 246 are communicatively coupled to a search service (e.g.,including a DFS master 250 or search service provider), which iscommunicatively coupled to a daemon 252 of the search head 244 tocollectively carry out operations of the cloud-based system 242.

The cloud-based system 242 includes index cache components 254. Theindex cache components 254 are communicatively coupled to cloud storage256, which can form a global index 258. The index cache components 254are analogous to indexers, and the cloud storage 256 is analogous tointernal data stores described elsewhere in this disclosure. The indexcache components 254 are communicatively coupled to the cloud workernodes 246, which can collect partial search results from the cloudstorage 256 by applying a search query to the index cache components254.

Lastly, the cloud worker nodes 246 can be communicatively coupled to oneor more external data sources 260. In some embodiments, only some of thecloud worker nodes 246 are coupled to the external data sources 260while others are only coupled to the index cache components 254. Forexample, the cloud worker nodes 246-1 and 246-3 are coupled to both theexternal data sources 260 and the index cache component 254, while thecloud worker node 246-2 is coupled to the index cache component 254-1but not the external data sources 260.

The scale of the cloud-based system 242 can be changed dynamically asneeded based on any number of metrics. For example, the scale can changebased on pricing constraints. In another example, the scale of the EMRcluster of nodes can be configured to improve the performance of searchoperations. For example, the cloud-based system 242 can scale the EMRcluster depending on the scope of a search query to improve theefficiency and performance of search processing.

In some embodiments, the EMR clusters can have access to flexible datastores such as a Hadoop distributed file system (HDFS), Amazon simplestorage services (S3), NoSQL, SQL, and custom SQL. Moreover, in someembodiments, the cloud-based system 242 can allow for a sharded query ofdata within these flexible data stores in a manner which makes scalingand aggregating partial search results (e.g., merging) most efficientwhile in place (e.g., reduces shuffling of partial search resultsbetween cloud worker nodes).

8.1. Cloud Deployment Operations FIG. 28 is a flow diagram illustratingan example of a method performed in a cloud-based DFS system(“cloud-based system”) according to some embodiments of the presentdisclosure. The operations of the cloud-based system are analogous tothose described elsewhere in this disclosure with reference to otherembodiments and, as such, a person skilled in the art would understandthose operations in the context of a cloud deployment. Accordingly, adescription of the flow diagram 3000 highlights some distinctions of thecloud deployment over other embodiments described herein.

In step 3002, the search head of the cloud-based system receives asearch query. In step 3004, the cloud-based system determines the typeof EMR cluster to use based on the scope of the received search query.For example, the cloud-based system can support two different types ofEMR clusters. In a first type scenario, a single large EMR cluster couldbe used for all search operations. In a second type scenario, subsets ofsmaller EMR clusters can be used for each type of search load. That is,a smaller subset of an EMR cluster can be used for a less complexaggregation processing of partial search results from different datasources. In some embodiments, the scale of an EMR cluster for the firstor second type can be set for each search load by a user or based on arole quota. In other words, the scale of the EMR cluster can depend onthe user submitting the search query and/or the user's designated rolein the cloud-based system.

In step 3006, the cloud-based system is dynamically scaled based on theneeds determined from the received search query. For example, the searchheads or cloud worker nodes can be scaled under the control of a searchservice to grow or shrink as needed based on the scale of the EMRcluster used to process search operations.

In step 3008, the cloud worker nodes can collect the partial searchresults from various data sources. Then, in step 3010, the cloud workernodes can aggregate the partial search results collected from thevarious data sources. Since the cloud worker nodes can scaledynamically, this allows for aggregating (e.g., merging) partial searchresults in an EMR cluster of any scale.

In step 3012, the resulting aggregated search results can be computedand reported at scale to the search head or search service provider.Thus, the cloud-based system can ensure that data (e.g., partial searchresults) from diverse data sources (e.g., including time-indexed eventswith raw data or other type of data) are reduced (e.g., combined) atscale on each EMR node of the EMR cluster before sending the aggregatedsearch results to the search head or search service provider.

The cloud-based system may include various other features that improveon the data intake and query system extended by the DFS system. Forexample, in some embodiments, the cloud-based system can collect metricswhich can allow for a heuristic determination of spikes in DFS searchrequirements. The determination can also be accelerated throughauto-scaling of the EMR clusters.

In some embodiments, the cloud-based system can allow DFS apps of thedata intake and query system to be bundled and replicated over an EMRcluster to ensure that they are executed at scale. Lastly, thecloud-based system can include mechanisms that allow user- orrole-quota-honoring based on a live synchronization between the dataintake and query system user management features and a cloud accesscontrol features.

9.0. Timeline Visualization

The disclosed embodiments include techniques for organizing andpresenting search results obtained from within a big data ecosystem viaa data intake and query system. In particular, a data intake and querysystem may cause output of the search results or data indicative of thesearch results on a display device. An example of a display device isthe client device 22 shown in FIG. 1A connected to the data intake andquery system 16 over the network 33.

For example, the data intake and query system 16 can receive a searchquery input by a user at the client device 22. The data intake and querysystem 16 can run the query on distributed data systems to obtain searchresults. The search results are then communicated to the client device22 over the network 33. The search results can be rendered in a visualway on the display of the client device 22 using items such as windows,icons, menus, and other graphics or controls.

For example, a client device can run a web browser that renders awebsite, which can grant a user access to the data intake and querysystem 16. In another example, the client device can run a dedicatedapplication that grants a user access to the data intake and querysystem 16. In either case, the client device can render a graphical userinterface (GUI), which includes components that facilitate submittingsearch queries, and facilitate interacting with and interpreting searchresults obtained by applying the submitted search queries on distributeddata systems of a big data ecosystem.

The disclosed embodiments include a timeline tool for visualizing thesearch results obtained by applying a search query to a combination ofinternal data systems and/or external data systems. The timeline toolincludes a mechanism that supports visualizing the search results byorganizing the search results in a time-ordered manner. For example, thesearch results can be organized into graphical time bins. The timelinetool can present the time bins and the search results contained in oneor more time bins. Hence, the timeline tool can be used by an analyst tovisually investigate structured or raw data events which can be ofinterest to the analyst.

The timeline mechanism supports combining timestamped andnon-timestamped search results obtained from diverse data systems topresent a visualization of the combined search results. For example, asearch query may be applied to the external data systems that each usedifferent compute resources and run different execution engines. Thetimeline mechanism can harmonize the search results from these datasystems, and a GUI rendered on a display device can present theharmonized results in a time-ordered visualization.

FIG. 29 is a flowchart illustrating a timeline mechanism that supportsrendering search results in a time-ordered visualization according tosome embodiments of the present disclosure. For example, the search headcan dictate to the DFS master whether a cursored or reporting searchshould be executed, or a search service provider can make thisdetermination. The search service provider can define a search schemeand/or search process and create a DAG. The DAG can orchestrate thesearch operations performed by the worker nodes for the cursored orreporting search.

In step 3102, the search service receives an indication that a requestfor a timeline visualization was received by the data intake and querysystem. For example, a user may input a request for a timelinevisualization before, after, or when a search query is input at a clientdevice. In another example, the data intake and query systemautomatically processes time-ordered requests to visualize in a timeline

In step 3104, the search service determines whether the requestedvisualization is for the search results of a cursored search or atime-ordered reporting search. For example, a cursored search may queryindexers of the data intake and query system as well as external datastores for a combination of time ordered partial search results. Inanother example, a time-ordered reporting search may require queryingthe indexers and external data stores for a time-ordered statistic basedon the combination of time ordered partial search results.

The search results for the timeline tool can be obtained in accordancewith a “Fast,” “Smart,” or “Verbose” search mode depending on whether acursored search or a reporting search was received. In particular, acursored search supports all three modes whereas a reporting search mayonly support the Verbose mode. The Fast mode prioritizes performance ofthe search and does not return nonessential search results. This meansthat the search returns what is essential and required. The Verbose modereturns all of the field and event data it possibly can, even if thesearch takes longer to complete, and even if the search includesreporting commands. Lastly, the default Smart mode switches between theFast and Verbose modes depending on the type of search being run (e.g.,cursored or reporting).

In step 3106, if the search is a cursored search, the search servicecreates buckets for the search results obtained from distributed datasystems. The buckets are created based on a timespan value. The timespanvalue may be a default value or a value selected by a user. For example,a timespan value may be 24 hours. The buckets may each represent adistinct portion of the timespan. For example, each bucket may representa distinct hour over a time-span of 24 hours.

The number of buckets that are created may be a default value dependingon the timespan, or depending on the number of data systems from whichsearch results were collected. For example, a default number of buckets(e.g., 1,000 buckets) may be created to span a default or selectedtimespan. In another example, distinct and unique buckets are createdfor portions of the timespan. In another example, a unique bucket iscreated per data system. In yet another example, buckets are created forthe same portion of the timespan but for different data systems.

In step 3108, search results obtained by application of the search queryto the different data systems are collected into the search buckets. Forexample, each bucket can collect the partial search results fromdifferent data systems that are timestamped with values within the rangeof the bucket. As such, the buckets support the timeline visualizationby organizing the search results.

In step 3110, the search service transfers a number of search resultscontained in the buckets to the search head. However, the search servicemay need to collect all the search results from across the data systemsinto the buckets before transferring the search results to the searchhead to ensure that the timeline visualization is rendered accurately.Moreover, the search results of the bucket may be transferred from thebuckets in chronological order. For example, the contents of the bucketsrepresenting beginning of the timespan are transferred first, and thecontents of the next buckets in time are transferred next, and so on.

In some embodiments, the number of search results transferred to thesearch head from the buckets may be a default or maximum value. Forexample, the first 1,000 search results from the buckets at thebeginning of the timespan may be first transferred to the search headfirst. In some embodiments, the search service transfer a maximum numberof search results per bin to the search head. In other words, the numberof search results transferred to the search head corresponds to themaximum number that can be contained in one or more bin of the timelinevisualization. Lastly, in step 3112, the search results of the reportingsearch received by the search head from the buckets are rendered in atimeline visualization.

In step 3114, if the search is a time-ordered reporting search, thesearch service creates buckets for the search results obtained fromdistributed data systems. The buckets can be created based on the numberof shards or partitions from which the search results are collected.

In step 3116, the search results are collected from across thepartitions. For external data sources, partial search results (e.g.,treated as raw events) are collected from across the shards/partitionsin time-order and transferred to the timeline mechanism. In case ofexternal data systems which have the capability to support shardedpartitions, multiple worker nodes can request for each specific shard orpartition. If needed, each partition can be sorted based on userspecified constraints such as, for example, a time ordering constraint.For sorting purposes, sometimes instead of unique shards, the DFS systemcan provide overlapping shards. For overlapping buckets across multipledata sources, the search service may need to collect partial searchresults across the different data sources before sending search resultsto the search head.

In step 3118, the search service transfers a number of search resultscontained in the buckets to the search head. However, the search servicemay need to collect all the search results from across the data systemsinto the buckets before transferring the search results to the searchhead to ensure that the timeline visualization is rendered accurately.Moreover, the search results of the bucket may be transferred from thebuckets in chronological order. For example, the contents of the bucketsrepresenting beginning of the timespan are transferred first, and thecontents of the next buckets in time are next, and so on.

In some embodiments, the number of search results transferred to thesearch head from the buckets may be a default or maximum value. Forexample, the first 1,000 search results from the buckets at thebeginning of the timespan may be first transferred to the search headfirst. In some embodiments, the search service transfers a maximumnumber of search results per bin to the search head. In other words, thenumber of search results transferred to the search head corresponds tothe maximum number that can be contained in one or more bin of thetimeline visualization. Lastly, in step 3120, the search results of thereporting search received by the search head from the buckets arerendered in a timeline visualization.

FIG. 30 illustrates a timeline visualization rendered on a userinterface 62 in which an embodiment may be implemented. The timelinevisualization presents event data obtained in accordance with a searchquery submitted to a data intake and query system. In the illustratedembodiment, the search query is input to search field 64 using SPL, inwhich a set of inputs is operated on by a first command line, and then asubsequent command following the pipe symbol “I” operates on the resultsproduced by the first command, and so on for additional commands. Asshown, a command on the left of the pipe symbol can set the scope of thesearch, which could include external data systems. Other commands on theright of the pipe symbol (and subsequent pipe symbols) can specify afield name and/or statistical operation to perform on the data sources.

In some embodiments, the search head or search service provider canimplement specific mechanism to parse the SPL. The search head or searchservice provider can determine that some portion of the search query isto be executed on the worker nodes base on the scope of the searchquery. In some embodiments, the search query can include a specificsearch command that triggers the search head to realize which portion ofthe search query should be executed by the DFS system. As a result, thephase generator can define the search phases, and where each of thosephases will be executed. In addition, once the phase generator decidesan operation needs to be executed by the DFS system, the search head orsearch service provider can optimize to push as much of the searchoperation as possible, for example, first to the external data sourceand then to the DFS system. In some embodiments, only the commands notincluded in the DFS command set will be executed back on the search heador search service provider once the results are retrieved to the searchhead or search service provider.

The timeline visualization presents multiple dimensions of data in acompact view, which reduced the cognitive burden on analysts viewing acomplex collection of data from internal and/or external data systems.That is, the timeline visualization provides a single unified view tofacilitate analysis of events stored across the big data ecosystem.Moreover, the timeline visualization includes selectable components tomanipulate the view in a manner suitable for the needs of an analyst.

The timeline visualization includes a graphic 66 that depicts a summaryof the search results in a timeline lane (e.g., in the form of rawevents), as well as a list of the specific search results 68. As shown,the timeline summary of the search results are presented as rectangularbins that are chronologically ordered and span a period of time (e.g.,Sep. 5, 2016 5:00 PM through Sep. 6, 2016 3:00 PM). The height of a binrepresents the magnitude of the quantity of events in that grouprelative to another group arranged along the timeline. As such, theheight of each bin indicates a count of events for a subset of theperiod of events relative to other counts for other bins within theperiod of time. The events in a group represented by a bin may have atimestamp value included in the range of time values of thecorresponding bin. Below the timeline summary is a listing of events ofthe search results presented in chronological order.

FIG. 31 illustrates a selected bin 68 of the timeline visualization andthe contents of the selected bin 70 according to some embodiments of thepresent disclosure. Specifically, the timeline visualization may includegraphic components that enable an analyst to investigate additionaldimensions of the search results summarized in the timeline. As shown,each bin representing a group of events may be selectable by an analyst.Selecting a bin may cause the GUI to display the specific group ofevents associated with the bin in the list below the timeline summary.Specifically, selecting a bin may cause the GUI to display the events ofthe search results that are timestamped within a range of thecorresponding group.

The timeline visualization is customizable and adaptable to presentsearch results in various convenient manners. For example, a user canchange the ordering of groups of events to obtain a differentvisualization of the same groups. In another example, a user can changethe range of the timeline to obtain a filtered visualization of thesearch results. In yet another example, a user can hide some events toobtain a sorted visualization of a subset of the search results.

In some embodiments, the activity for each data system may appear in aseparate timeline lane. If an activity start-time and duration areavailable for a particular data system, the respective timeline may showa duration interval as a horizontal bar in the lane. If a start time isavailable, the timeline visualization may render an icon of that time onthe visualization. As such, the timeline visualization can be customizedand provide interactive features to visualize search results, andcommunicate the results in dashboards and reports.

Thus, the timeline visualization can support a timeline visualization ofexternal data systems, where each external data system may operate usingdifferent compute resources and engines. For example, the timelinevisualization can depict search results obtained from one or moreexternal data systems, collated and presented in a single and seamlessvisualization. As such, the timeline visualization is a tool ofunderlying logic that facilitates investigating events obtained from anyof the external data systems, internal data systems (e.g., indexers), ora combination of both.

The underlying logic can manage and control the timeline visualizationrendered on the GUI in response to data input and search resultsobtained from within the big data ecosystem. In some embodiments, theunderlying logic is under the control and management of the data intakeand query system. As such, an analyst can interface with the data intakeand query system to use the timeline visualization. For example, thetimeline logic can cause the timeline visualization to render activitytime intervals and discrete data events obtained from various datasystem resources in internal and/or external data systems.

The underlying logic includes the search service. Since the bins mayinclude events data from multiple data systems, each bin can representan overlapping bin across multiple data systems. Accordingly, the searchservice can collect the data events across the different data systemsbefore sending them to the search head. To finalize a search operation,the search service may transmit the maximum number of events per bin orthe maximum size per bin to the search head.

In some embodiments, the underlying logic uses the search head of thedata intake and query system to collect data events from the variousdata systems that are presented on the timeline visualization. In someembodiments, the events are collected in accordance with any of themethods detailed above, and the timeline visualization is a portal forviewing the search results obtained by implementing those methods. Assuch, the collected events can have timestamps indicative of, forexample, times when the event was generated.

The timestamps can be used by the underlying logic to sort the eventsinto the bins associated with any parameter such as a time range. Forexample, the underlying logic may include numerous bins delineated byrespective chronological time ranges over a total period of time thatincludes all the bins. In some embodiments, a maximum amount of eventstransferred into the time bins could be set.

In some embodiments, the underlying logic of the timeline visualizationcan automatically create bins for a default timespan in response tocursored searches of ordered data. For example, an analyst may submit acursored search, and the underlying logic may cause the timelinevisualization to render a display for events within a default timespan.The amount and rate at which the events are transferred to the searchhead for subsequent display on the timeline visualization could varyunder the control of the underlying logic. For example, a maximum numberof events could be transferred on a per bin basis by the worker nodes tothe search head. As such, the DFS system could balance the load on thenetwork.

In some embodiments, the underlying logic of the timeline visualizationcan utilize the sharding mechanism detailed above for reporting searchesof ordered data from external data systems. Specifically, the data couldbe sharded across partitions in response to a reporting search, whereexecutors have overlapping partitions. Further, the underlying logic maycontrol the search head or search service provider to collect the eventsdata across the shards/partitions in time order for rendering on thetimeline visualization. Under either the cursored search or reportingsearch, the underlying logic may impose the maximum size of total eventstransferred into bins.

10.0. Monitoring and Metering Services

The disclosed embodiments also include monitoring and metering servicesof the DFS system. Specifically, these services can include techniquesfor monitoring and metering metrics of the DFS system. The metrics arestandards for measuring use or misuse of the DFS system. Examples of themetrics include data or components of the DFS system. For example, ametric can include data stored or communicated by the DFS system orcomponents of the DFS system that are used or reserved for exclusive useby customers. The metrics can be measured with respect to time orcomputing resources (e.g., CPU utilization, memory usage) of the DFSsystem. For example, a DFS service can include metering the usage ofparticular worker nodes by a customer over a threshold period of time.

In some embodiments, a DFS service can meter the amount hours that aworker node spends running one or more tasks (e.g., a search requests)for a customer. In another example, a DFS service can meter the amountof resources used to run one or more tasks rather than, or incombination with, the amount of time taken to complete the task(s). Insome embodiments, the licensing approaches include the total DFS hoursused per month billed on a per hour basis; the maximum capacity that canbe run at any one time, e.g. the total number of workers with a cap onthe amount of size of each worker defined by CPU and RAM available tothat worker; and finally a data volume based approach where the customeris charged by the amount of data brought into the DFS for processing.

FIG. 32 is a flow diagram illustrating monitoring and metering servicesof the DFS system according to some embodiments of the presentdisclosure. In the illustrated embodiment, in step 3202, the DFSservices can monitor one or more metrics of a DFS system. The DFSservices can monitor the DFS system for a variety of reasons. Forexample, in step 3204, a DFS service can track metrics and/or displaymonitored metrics or data indicative of the monitored metrics. Hence,the metrics can be preselected by, for example, a system operator oradministrator seeking to analyze system stabilities, instabilities, orvulnerabilities.

In some embodiments, the DFS services can meter use of the DFS system asa mechanism for billing customers. For example, in step 3206, the DFSservices can monitor specific metrics for specific customers that usethe DFS system. The metering services can differ depending on whetherthe customer has a subscription to use the DFS system or is using theDFS system on an on-demand basis. As such, a DFS service can run avalue-based licensing agreement that allows customers to have a fairexchange of value for their use of the DFS service.

In step 3208, a determination is made about whether a customer has asubscription to use the DFS system. The subscription can define thescope of a license granted to a customer to access or use the DFSsystem. The scope can define an amount of functionality available to thecustomer. The functionality can include, for example, the number ortypes of searches that can be performed on the DFS system. In someembodiments, the scope granted to a user can vary in proportion to cost.For example, customers can purchase subscriptions of different scope fordifferent prices, depending on the needs of the customers. As such, aDFS service can run a value-based licensing agreement that allowscustomers to have a fair exchange of value for their use of the DFSservice.

In step 3210, if the customer is subscribed, the DFS service can metermetrics based on a subscription purchased by the customer. For example,a subscription to a DFS service may limit the amount of searches that acustomer can submit to the DFS system. As such, the DFS service willmeter the number of searches that are submitted by the customer. Inanother example, a subscription to the DFS service may limit the time auser can actively access a DFS service. As such, the DFS service willmeter the amount of time that a user spends actively using the DFSservice.

In step 3212, a DFS service determines whether the customer's use of theDFS system exceeded a threshold amount granted by the subscription. Forexample, a customer may exceed the scope of a paid subscription by usingfunctionality not included in the paid subscription or using morefunctionality than that granted by the subscription. In someembodiments, the excess use can be measured with respect to a metricsuch as time or use of computing resources.

In step 3212, a DFS service determines whether a customer exceeded thescope of the customer's subscription. In step 3214, if the customer didnot exceed the subscription, no action is taken (e.g., the customer isnot charged additional fees). Referring back to step 3212, a variety ofactions can be taken if the customer has exceed the subscription. Instep 3216, the DFS service can charge the customer for the excess amountof the metered metric. For example, the DFS service may begin meteringthe amount of time a customer spends using the DFS system after athreshold amount of time has been exceeded. In step 3218, the DFSservice can alternatively or additionally prevent the customer fromaccessing the DFS system if the customer exceeds the subscription or hasnot paid the additional charges of step 3216.

Referring back to step 3208, if the customer is not subscribed to a DFSsubscription service, then customer may still access the DFS systemthrough a variety of other techniques. For example, a DFS service mayprovide limited or temporary access to the DFS system to anon-subscribed customer. In another example, a DFS service may provideaccess to the DFS service on-demand.

Either way, in step 3220, a DFS service meters metrics on anon-subscription basis. For example, in step 3222, the customer can payfor each instance the customer uses the DFS system. In another example,in step 3224, a DFS service can start charging a non-subscribed customerfor using the DFS system once the metrics of the service exceed athreshold amount. For example, a DFS service may provide free limitedaccess or temporary full access to the DFS system. When the measuringmetrics exceed the free limited access, the customer may be charged foraccess that exceeds the free amount. In either case, in step 3218, theDFS service can prevent the customer from accessing the DFS system ifthe measuring metrics exceed the threshold amount or the customer hasnot paid the charges of step 3222 or 3224. In some embodiments, a DFSserver can allow the customer to complete an active search that exceededa measuring metric but deny the customer from using the DFS system anyfurther until additional payment authorized.

11.0. Data Intake and Fabric System Architecture

FIG. 33 is a system diagram illustrating an environment 3300 foringesting and indexing data, and performing queries on one or moredatasets from one or more dataset sources. In the illustratedembodiment, the environment 3300 includes data sources 201, clientdevices 404, described in greater detail above with reference to FIG. 4, and external data sources 3318 communicatively coupled to a dataintake and query system 3301. The external data sources 3318 can besimilar to the external data systems 12-1, 12-2 described above withreference to FIG. 1A or the external data sources described above withreference to FIG. 4

In the illustrated embodiment, the data intake and query system 3301includes any combination of forwarders 204, indexers 206, data stores208, and a search head 210, as discussed in greater detail above withreference to FIGS. 2-4 . For example, the forwarders 204 can forwarddata from the data sources 202 to the indexers 206, the indexers can 206ingest, parse, index, and store the data in the data stores 208, and thesearch head 210 can receive queries from, and provide the results of thequeries to, client devices 404 on behalf of the system 3301.

In addition to forwarders 204, indexers 206, data stores 208, and thesearch head 210, the system 3301 further includes a search processmaster 3302 (in some embodiments also referred to as DFS master), one ormore query coordinators 3304 (in some embodiments also referred to assearch service providers), worker nodes 3306, and a query accelerationdata store 3308. In some embodiments, a workload advisor 3310, workloadcatalog 3312, node monitor 3314, and dataset compensation module 3316can be included in the search process master 3302. However, it will beunderstood that any one or any combination of the workload advisor 3310,workload catalog 3312, node monitor 3314, and dataset compensationmodule 3316 can be included elsewhere in the system 3301, such as in asa separate device or as part of a query coordinator 3304.

As will be described in greater detail below, the functionality of thesearch head 210 and the indexers 206 in the illustrated embodiment ofFIG. 33 can differ in some respects from the functionality describedpreviously with respect to other embodiments. For example, in theillustrated embodiment of FIG. 33 , the search head 210 can perform someprocessing on the query and then communicate the query to the searchprocess master 3302 and coordinator(s) 3304 for further processing andexecution. For example, the search head 210 can authenticate the clientdevice or user that sent the query, check the syntax and/or semantics ofthe query, or otherwise determine that the search request is valid. Insome cases, a daemon running on the search head 210 can receive a query.In response, the search head 210 can spawn a search process to furtherhandle the query, including communicating the query to the searchprocess master 3302 or query coordinator 3304. Upon completion of thequery, the search head 210 can receive the results of the query from thesearch process master 3302 or query coordinator 3304 and serve theresults to the client device 404. In such embodiments, the search head210 may not perform any additional processing on the results receivedfrom the search process master 3302 or query coordinator 3304. In somecases, upon receiving and communicating the results, the search head 210can terminate the search process.

In addition, the indexers 206 in the illustrated embodiment of FIG. 33can receive the relevant subqueries from the query coordinator 3304rather than the search head 210, search the corresponding data stores208 for relevant events, and provide their individual results of thesearch to the worker nodes 3306 instead of the search head 210 forfurther processing. As described previously, the indexers 206 cananalyze events for a query in parallel. For example, each indexer 206can search its corresponding data stores 208 in parallel and communicateits partial results to the worker nodes 3306.

The search head 210, search process master 3302, and query coordinator3304 can be implemented using separate computer systems, processors, orvirtual machines, or may alternatively comprise separate processesexecuting on one or more computer systems, processors, or virtualmachines. In some embodiments, running the search head 210, searchprocess master 3302, and/or query coordinator 3304 on the same machinecan increase performance of the system 3301 by reducing communicationsover networks. In either case, the search process master 3302 and querycoordinator 3304 can be communicatively coupled to the search head 210.

The search process master 3302 and query coordinator 3304 can be used toreduce the processing demands on the search head 210. Specifically, thesearch process master 3302 and coordinator 3304 can perform some of thepreliminary query processing to reduce the amount of processing done bythe search head 210 upon receipt of a query. In addition, the searchprocess master 3302 and coordinator 3304 can perform some of theprocessing on the results of the query to reduce the amount ofprocessing done by the search head 210 prior to communicating theresults to a client device. For example, upon receipt of a query, thesearch head 210 can determine that the query can be processed by thesearch process master 3302. In turn, the search process master 3302 canidentify a query coordinator 3304 that can process the query. In somecases, if there is not a query coordinator 3304 that can handle theincoming query, the search process master 3302 can spawn an additionalquery coordinator 3304 to handle the query.

The query coordinator(s) 3304 can coordinate the various tasks toexecute queries assigned to them and return the results to the searchhead 210. For example, as will be described in greater detail below, thequery coordinator 3304 can determine the amount of resources availablefor a query, allocate resources for the query, determine how the queryis to be broken up between dataset sources, generate commands for thedataset sources to execute, determine what tasks are to be handled bythe worker nodes 3306, spawn the worker nodes 3306 for the differenttasks, instruct different worker nodes 3306 to perform the differenttasks and where to route the results of each task, monitor the workernodes 3306 during the query, control the flow of data between the workernodes 3306, process the aggregate results from the worker nodes 3306,and send the finalized results to the search head 210 or to anotherdataset destination. In addition, the query coordinators 3304 caninclude providing data isolation across different searches based onrole/access control, as well as fault tolerance (e.g., localized to asearch head). For example, if a search operation fails, then its spawnedquery coordinator 3304 may fail but other query coordinators 3304 forother queries can continue to operate. In addition, queries that are tobe isolated from one another can use different query coordinators 3304.

The worker nodes 3306 can perform the various tasks assigned to them bya query coordinator 3304. For example, the worker nodes 3306 can intakedata from the various dataset sources, process the data according to thequery, collect results from the processing, combine results from variousoperations, route the results to various destinations, etc. In certaincases, the worker nodes 3306 and indexers 206 can be implemented usingseparate computer systems, processors, or virtual machines, or mayalternatively comprise separate processes executing on one or morecomputer systems, processors, or virtual machines.

The query acceleration data store 3308 can be used to store datasets foraccelerated access. In some cases, the worker nodes 3306 can obtain datafrom the indexers 206, external data sources 3318, or other location(e.g., common storage, ingested data buffer, etc.) and store the data inthe query acceleration data store 3308. In such embodiments, when aquery is received that relates to the data stored in the queryacceleration data store 3308, the worker nodes 3306 can access the datain the query acceleration data store 3308 and process the data accordingto the query. Furthermore, if the query also includes a request fordatasets that are not in the query acceleration data store 3308, theworker nodes 3306 can begin working on the dataset obtained from thequery acceleration data store 3308, while also obtaining the otherdataset(s) from the other dataset source(s). In this way, a clientdevice 414 a-404 n can rapidly receive a response to a provided query,while the worker nodes 3306 obtain datasets from the other datasetsources.

The query acceleration data store 3308 can be, for example, adistributed in-memory database system, storage subsystem, and so on,which can maintain (e.g., store) datasets in both low-latency memory(e.g., random access memory, such as volatile or non-volatile memory)and longer-latency memory (e.g., solid state storage, disk drives, andso on). To increase efficiency and response times, the accelerated dataset 3308 can maintain particular datasets in the low-latency memory, andother datasets in the longer-latency memory. For example, the datasetscan be stored in-memory (non-limiting examples: RAM or volatile memory)with disk spillover (non-limiting examples: hard disks, disk drive,non-volatile memory, etc.). In this way, the query acceleration datastore 3308 can be used to serve interactive or iterative searches. Insome cases, datasets which are determined to be frequently accessed by auser can be stored in the lower-latency memory. Similarly, datasets ofless than a threshold size can be stored in the lower-latency memory.

As will be described below, a user can indicate in a query thatparticular datasets are to be stored in the query acceleration datastore 3308. The query can then indicate operations to be performed onthe particular datasets. For subsequent queries directed to theparticular datasets (e.g., queries that indicate other operations), theworker nodes 3306 can obtain information directly from the queryacceleration data store 3308. Additionally, since the query accelerationdata store 3308 can be utilized to service requests from differentclients 404 a-404 n, the query acceleration data store 3308 canimplement access controls (e.g., an access control list) with respect tothe stored datasets. In this way, the stored datasets can optionally beaccessible only to users associated with requests for the datasets.Optionally, a user who provides a query can indicate that one or moreother users are authorized to access particular requested datasets. Inthis way, the other users can utilize the stored datasets, thus reducinglatency associated with their queries.

In certain embodiments, the worker nodes 3306 can store data from anydataset source, including data from a dataset source that has not beentransformed by the nodes 3306, processed data (e.g., data that has beentransformed by the nodes 3306), partial results, or aggregated resultsfrom a query in the query acceleration data store 3308. In suchembodiments, the results stored in the query acceleration data store3308 can be served at a later time to the search head 210, combined withadditional results obtained from a later query, transformed or furtherprocessed by the worker nodes 3306, etc.

It will be understood that the system 3301 can include fewer or morecomponents as desired. For example, in some embodiments, the system 3301does not include a search head 210. In such embodiments, the searchprocess master 3302 can receive query requests from clients 404 andreturn results of the query to the client devices 404. Further, it willbe understood that in some embodiments, the functionality describedherein for one component can be performed by another component. Forexample, although the workload advisor 3310 and dataset compensationmodule 3316 are described as being implemented in the search processmaster 3302, it will be understood that these components and theirfunctionality can be implemented in the query coordinator 3304.Similarly, as will be described in greater detail below, in someembodiments, the nodes 3306 can be used to index data and store it inone or more data stores, such as the common storage or ingested databuffer, described in greater detail below.

11.1. Worker Nodes

FIG. 34 is a block diagram illustrating an embodiment of multiplemachines 3402, each having multiple nodes 3306-1, 3306-n (individuallyand collectively referred to as node 3306 or nodes 3306) residingthereon. The worker nodes 3306 across the various machines 3402 can becommunicatively coupled to each other, to the various components of thesystem 3301, such as the indexers 206, query coordinator 3304, searchhead 210, common storage, ingested data buffer, etc., and to theexternal data sources 3318.

The machines 3402 can be implemented using multi-core servers orcomputing systems and can include an operating system layer 3404 withwhich the nodes 3306 interact. For example, in some embodiments, eachmachine 3402 can include 32, 48, 64, or more processor cores, multipleterabytes of memory, etc.

In the illustrated embodiment, each node 3306 includes four processors3406, memory 3408, a monitoring module 3410, and aserialization/deserialization module 3412. It will be understood thateach node 3306 can include fewer or more components as desired.Furthermore, it will be understood that the nodes 3306 can includedifferent components and resources from each other. For example node3306-1 can include fewer or more processors 3406 or memory 3408 than thenode 3306-n.

The processors 3406 and memory 3408 can be used by the nodes 3306 toperform the tasks assigned to it by the query coordinator 3304 and cancorrespond to a subset of the memory and processors of the machine 3402.The serialization/deserialization module 3412 can be used toserialize/deserialize data for communication between components of thesystem 3301, as will be described in greater detail below.

The monitoring module 3410 can be used to monitor the state andutilization rate of the node 3306 or processors 3406 and report theinformation to the search process master 3302 or query coordinator 3304.For example, the monitoring module 3410 can indicate the number ofprocessors in use by the node 3306, the utilization rate of eachprocessor, whether a processor is unavailable or not functioning, theamount of memory used by the processors 3406 or node 3306, etc.

In addition, each worker node 3306 can include one or more softwarecomponents or modules (“modules”) operable to carry out the functions ofthe system 3301 by communicating with the query coordinator 3304, theindexers 206, and the dataset sources. The modules can run on aprogramming interface of the worker nodes 3306. An example of such aninterface is APACHE SPARK, which is an open source computing frameworkthat can be used to execute the worker nodes 3306 with implicitparallelism and fault-tolerance.

In particular, SPARK includes an application programming interface (API)centered on a data structure called a resilient distributed dataset(RDD), which is a read-only multiset of data items distributed over acluster of machines (e.g., the devices running the worker nodes 3306).The RDDs function as a working set for distributed programs that offer aform of distributed shared memory.

Based on instructions received from the query coordinator 3304, theworker nodes 3306 can collect and process data or partial search resultsof a distributed network of data storage systems, and provide aggregatedpartial search results or finalized search results to the querycoordinator 3304 or other destination. Accordingly, the querycoordinator 3304 can act as a manager of the worker nodes 3306,including their distributed data storage systems, to extract, collect,and store partial search results via their modules running on acomputing framework such as SPARK. However, the embodiments disclosedherein are not limited to an implementation that uses SPARK. Instead,any open source or proprietary computing framework running on acomputing device that facilitates iterative, interactive, and/orexploratory data analysis coordinated with other computing devices canbe employed to run the modules 218 for the query coordinator 3304 toapply search queries to the distributed data systems.

As a non-limiting example, as part of processing a query, a node 3306can receive instructions from a query coordinator 3304 to perform one ormore tasks. For example, the node 3306 can be instructed to intake datafrom a particular dataset source, parse received data from a datasetsource to identify relevant data in the dataset, collect partial resultsfrom the parsing, join results from multiple datasets, or communicatepartial or completed results to a destination, etc. In some cases, theinstructions to perform a task can come in the form of a DAG. Inresponse, the node 3306 can determine what task it is to perform in theDAG, and execute it.

As part of performing the assigned task, the node 3306 can determine howmany processors 3406 to allocate to the different tasks. In someembodiments the node can determine that all processors 3406 are to beused for a particular task or only a subset of the processors 3406. Incertain embodiments, each processor 3406 of the node 3306 can be used asa partition to intake, process, or collect data according to a task, orto process data of a partition as part of an intake, process, or collecttask. Upon completion of the task, the node 3306 can inform the querycoordinator 3304 that the task has been completed.

When instructed to intake data, the processors 3406 of the node 3306 canbe used to communicate with a dataset source (non-limiting examples:external data sources 3318, indexers 206, common storage, queryacceleration data store 3308, ingested data buffer, etc.). Once the node3306 is in communication with the dataset source it can intake the datafrom the dataset source. As described in greater detail below, in someembodiments, multiple partitions of a node (or different nodes) can beassigned to intake data from a particular source.

When instructed to parse or otherwise process data, the processors 3406of the node 3306 can be used to review the data and identify portions ofthe data that are relevant to the query. For example, if a queryincludes a request for events with certain errors or error types, theprocessors 3406 of the node 3306 can parse the incoming data to identifydifferent events, parse the different events to identify error fields orerror keywords in the events, and determine the error type of the error.In some cases, this processing can be similar to the processingdescribed in greater detail above with reference to the indexers 206processing data to identify relevant results in the data stores 208.

When instructed to collect data, the processors 3406 of the node 3306can be used to receive data from dataset sources or processing nodes.With continued reference to the error example, a collector partition, orprocessor 3406 can collect all of the errors of a certain type from oneor more parsing partitions or processors 3406. For example, if there areseven possible types of errors coming from a particular dataset source,a collector partition could collect all type 1 errors (or events with atype 1 error), while another collector partition could collect all type2 errors (or events with a type 2 error), etc.

When instructed to join results from multiple datasets, the processors3406 of the node 3306 can be used to receive data corresponding to twodifferent datasets and combine or further process them. For example, ifdata is being retrieved from an external data source and a data store208 of the indexers 206, join partitions could be used to compare andcollate data from the different data stores in order to aggregate theresults.

When instructed to communicate results to a particular destination, theprocessors 3406 of the node 3306 can be used to prepare the data forcommunication to the destination and then communicate the data to thedestination. For example, in communicating the data to a particulardestination, the node 3306 can communicate with the particulardestination to ensure the data will be received. Once communication withthe destination has been established, the partition, or processorassociated with the partition, can begin sending the data to thedestination. As described in greater detail below, in some embodiments,data from multiple partitions of a node (or different nodes) can becommunicated to a particular destination. Furthermore, the nodes 3306can be instructed to transform the data so that the destination canproperly understand and store the data. Furthermore, the nodes cancommunicate the data to multiple destinations. For example, one copy ofthe data may be communicated to the query coordinator 3304 and anothercopy can be communicated to the query acceleration data store 3308.

The system 3301 is scalable to accommodate any number of worker nodes3306. As such, the system 3301 can scale to accommodate any number ofdistributed data systems upon which a search query can be applied andthe search results can be returned to the search head and presented in aconcise or comprehensive way for an analyst to obtain insights into biddata that is greater in scope and provides deeper insights compared toexisting systems.

11.1.1. Serializatoin/Deserialization

In some cases, the serialization/deserialization module 3412 cangenerate and transmit serialized event groups. An event group caninclude the following information: number of events in the group, headerinformation, event information, and changes to the cache or cachedeltas. The serialization/deserialization module 3412 can identify thedifferences between the pieces of information using a type code ortoken. In certain cases, the type code can be in the form of a typebyte. For example, prior to identifying header information, theserialization/deserialization module 3412 can include a header type codeindicating that header information is to follow. Similarly, type codescan be used to identify event data or cache deltas.

The header information can indicate the number and order of fields inthe events, as well as the name of each field. Similarly, the eventinformation for each event can include the number of fields in theevent, as well as the value for that field. The cache deltas canidentify changes to make to the cache relied upon toserialize/deserialize the data.

As part of generating the group and serializing the data, theserialization/deserialization module 3412 can determine the number ofevents to group, determine the order and field names for the fields inthe events of the group, parse the events, determine the number offields for each event, identify and serialize serializable field valuesin the event fields, and identify cache deltas. In some cases, theserialization/deserialization module 3412 performs the various tasks ina single pass of the data, meaning that it performs the identification,parsing, and serializing during a single review of the data. In thismanner, the serialization/deserialization module 3412 can operate onstreaming data and avoid adding delay to theserialization/deserialization process.

In some embodiments, an event group includes an identifier indicatingthe number of events in the group followed by a header type code and anumber of fields indicating the number of fields in the events. For eachfield designated by the header, the event group can include a type codeindicating whether the field name is already stored in cache or a typecode indicating that the field name is included. Depending on the typecode, the event group can include an identifier or the field name. Forexample, if the type code indicates the field name is stored in cache(e.g., a cache code), an identifier can be included to enable areceiving component to lookup the field name using the cache. If thetype code indicates the field name is not stored in cache (e.g., a datacode), the name of the field name can be included.

Similar to the header information, for each event in the event group,the event group can include number of fields in the event. For eachfield of the event, the event group can include a type code indicatingwhether the field name is already stored in cache or a type codeindicating that the field name is included.

As mentioned above, the event group can also include cache deltainformation. The cache delta information can include a cache delta typecode indicating that the cache is to be changed, a number of newentries, and a number of dropped entries. For each new entry the cachedelta information can include the data or string being cached, and anidentifier for the data. For each entry being dropped, the cache deltainformation can include the identifier of the cache entry to be dropped.

As a non-limiting example, consider the following portions of events:

ronnie.sv.splunk.com, access_combined, SALE, World of Cheese, 14.95

ronnie.sv.splunk.com, access_combined, NO SALE, World of Cheese, 16.75

ronnie.sv.splunk.com, access_combined, SALE, World of Cheese

ronnie.sv.splunk.com, access_combined, SALE, Fondue Warrior, 20.95

In serializing the above-referenced events, theserialization/deserialization module 3412 can determine that the fieldnames for the events are source, sourcetype, sale type, company name,and price and that this information is not in cache. Theserialization/deserialization module 3412 can then generate thefollowing event group:

4 (number of events) Header_Code 5 (number of Data_Code “source” fields)Data_Code “sourcetype” Data_Code “sale_type” Data_Code “company name”Data_Code “price” Cache_Delta_Code 5 (entries to “source” x15 add)“sourcetype” x16 “sale_type” x17 “company name” x18 “price” x19 0(entries to drop) Event_Code 5 (number of Data_Code“ronnie.sv.splunk.com” fields in Data_Code “access_combined” event)Data_Code “SALE” Data_Code “World of Cheese” Data_Code “14.95”Cache_Delta_Code 5 (number of “ronnie.sv.splunk.com” x21 new entries)“access_combined” x22 “SALE” x23 “World of Cheese” x24 “14.95” x25 0(entries to drop) Event_Code 5 (number of Cache_Code x21 fields inCache_Code x22 event) Data_Code “NO SALE” Cache_Code x24 Data_Code“16.75” Cache_Delta_Code 2 (entries to “NO SALE” x26 add) “16.75” x27 0(entries to drop) Event_Code 4 (number of Cache_Code x21 fields inCache_Code x22 event) Cache_Code x23 Cache_Code x24 Event_Code 5 (numberof Cache_Code x21 fields in Cache_Code x22 event) Cache_Code x23Data_Code “World of Cheese” Data_Code “20.95” Cache_Delta_Code 2 (numberof “World of Cheese” new entries) “20.95” 1 (entry to x25 drop)

By generating the group, the serialization/deserialization module 3412can reduce the amount of data communicated for each group. For example,instead of transmitting the string “ronnie.sv.splunk.com” each time, theserialization/deserialization module 3412 serializes it and thencommunicates the cache ID thereafter.

Entries can be added or dropped using a variety of techniques. In somecases, every new field value is cached. In certain cases, a field valueis cached after it has been identified a threshold number of times.Similarly, an entry can be dropped after a threshold number of events orevent groups have been processed without the particular value beingidentified. As a non-limiting example, the serialization/deserializationmodule 3412 can track X values at a time in a cache C and track up to Yvalues at a time that are not cached and how many time those values havebeen identified in a candidate set D. When a value is received, if it isin the cache C, then the identifier can be returned. If the value is notin the cache C, then it can be added to D. If Y has been reached in D,then the least recently used value can be dropped. If the count of thevalue in D satisfies a threshold T, then it can be moved to the cache Cand receive an identifier. If the size of C is more than X, then theleast recently used value in C can be dropped.

In some embodiments, the cache is built as the data is processed, andchanges are transmitted as they occur. For example, the receiver canstart with an empty cache, and apply each delta as it comes along. Asmentioned above, each delta can have two sections: new entries, anddropped entries. In certain embodiments, the receiver (or deserializer)does not drop cache entries until told to do so, otherwise, it may notbe able interpret identifiers received from the serializer. In suchembodiments, the serializer performs cache maintenance by informing thedeserializer when to drop entries. Upon receipt of such a command, thedeserializer can remove the identified entries. 11.2. SEARCH PROCESSMASTER

As mentioned above, the search process master 3302 can perform variousfunctions to reduce the workload of the search head 210. For example,the search process master 3302 can parse an incoming query and allocatethe query to a particular query coordinator 3304 for execution or spawnan additional query coordinator 3304 to execute the query. In addition,the search process master 3302 can track and store information regardingthe system 3301, queries, external data stores, etc., to aid the querycoordinator 3304 in processing and executing a particular query. In someembodiments, the search process master 3302.

In some cases, the search process master 3302 can determine whether aquery coordinator 3304 should be spawned based on user information. Forexample, for data protection or isolation, the search process master3302 can spawn query coordinators 3304 for different users. In addition,the search process master 3302 can spawn query coordinators 3304 if itdetermines that a query coordinator 3304 is over utilized.

In some cases, to accomplish these various tasks the search processmaster 3302 can include a workload advisor 3310, workload catalog 3312,node monitor 3314, and dataset compensation module 3316. Althoughillustrated as being a part of the search process master 3302, it willbe understood that any one or any combination of these components can beimplemented separately or included in one or more query coordinators3304. Furthermore, although illustrated as individual components, itwill be understood that any one or any combination of the workloadadvisor 3310, workload catalog 3312, node monitor 3314, and datasetcompensation module 3316 can be implemented by the same machine,processor, or computing device.

As a brief introduction, the workload advisor 3310 can be used toprovide resource allocation recommendations to a query coordinator 3304for processing queries, the workload catalog 3312 can store data relatedto previous queries, the node monitor 3314 can receive information fromthe worker nodes 3306 regarding a current status and/or utilization rateof the nodes 3306, and the dataset compensation module 3316 can be usedby the query coordinator 3304 to enhance interactions with external datasources.

11.2.1 Workload Catalog

The workload catalog 3312 can store relevant information to aid theworkload advisor 3310 in providing a resource allocation recommendationto a query coordinator 3304. As queries are received and processed bythe system 3301, the workload catalog 3312 can store relevantinformation about the queries to improve the workload advisor's 3310ability to recommend the appropriate amount of resources for each query.For example, the system 3301 can track any one or any combination of thefollowing data points about a query: which dataset sources wereaccessed, what was accessed in each dataset source (particular tables,buckets, etc.), the amount of data retrieved from the dataset sources(individually and collectively), the time taken to obtain the data fromthe dataset sources, the number of nodes 3306 used to obtain the datafrom each dataset source, the utilization rate of the nodes 3306 whileobtaining the data from the dataset source, the number oftransformations or phases (processing, collecting, reducing, joining,branching, etc.) performed on the data obtained from the datasetsources, the time to complete each transformation, the number of nodes3306 assigned to each phase, the utilization rate of each node 3306assigned to the particular phase, the processing performed by the querycoordinator 3304 on results (individual or aggregatee), time to store ordeliver results to a particular destination, resources used tostore/deliver results, total time to complete query, time of day ofquery request, etc. Furthermore, the workload catalog can includeidentifying information corresponding to the datasets with which thesystem interacts (e.g., indexers, common storage, ingested data buffer,external data sources, query acceleration data store, etc.). Thisinformation can include, but is not limited to, relationships betweendatasets, size of dataset, rate of growth of dataset, type of data,selectivity of dataset, provider of dataset, indicator for privateinformation (e.g., personal health information, etc.), trustworthinessof a dataset, dataset preferences, etc.

The workload catalog 3312 can collect the data from the variouscomponents of the system 3301, such as the query coordinator 3304,worker nodes 3306, indexers 206, etc. For example, for each taskperformed by each node 3306, the node 3306 can report relevant timingand resource utilization information to the query coordinator 3304 ordirectly to the workload catalog 3312. Similarly, the query coordinator3304 can report relevant timing, usage, and data information for eachphase of a search, each transformation of data, or for a total query.

Using the information collected in the workload catalog 3312, theworkload advisor 3310 can estimate the compute cost to perform aparticular data transformation or query, or to access a particulardataset. Further, the workload advisor can determine the amount ofresources (nodes, memory, processors, partitions, etc.) to recommend fora query in order to provide the results within a particular amount oftime. 11.2.2 NODE MONITOR

The node monitor 3314 can also store relevant information to aid theworkload advisor 3310 in providing a resource allocation recommendation.For example, the node monitor 3314 can track and store informationregarding any one or any combination of: total number of processors ornodes in the system 3301, number of processors or nodes that are notavailable or not functioning, number of available processors or nodes,utilization rate of the processors or nodes, number of worker nodes,current tasks being completed by the worker nodes 3306 or processors,estimated time to complete a task by the nodes 3306 or processors,amount of available memory, total memory in the system 3301, tasksawaiting execution by the nodes 3306 or processors, etc.

The node monitor 3314 can collect the relevant information bycommunicating with the monitoring module 3410 of each node 3306 of thesystem 3301. As described above, the monitoring modules 3410 of eachnode 3306 can report relevant information about the node state andutilization rate. Using the information from the node monitor 3314, theworkload advisor 3310 can ascertain the general state of any particularprocessor, node, or the system 3301, and determine the number of nodes3306 or processors 3306 available for a particular task or query.

11.2.3 Dataset Compensation

As discussed above, the external data sources 3318 with which the system3301 can interact vary significantly. For example, some external datasource may have processing capabilities that can be used to perform someprocessing on the data that resides there prior to communicating thedata to the nodes 3306. In addition, the external data sources 3318 maysupport parallel reads from multiple partitions. Conversely, otherexternal data sources 3318 may not be able to perform much, if any,processing on the data contained therein and/or may only be able toprovide serial reads from a single partition. Additionally, eachexternal data source 3318 may have particular requirements forinteracting with it, such as a particular API, throttling requirements,etc. Further, the type and amount of data stored in each external datasource 3318 can vary significantly. As such, the system's 3301interaction with the different external data sources 3318 can varysignificantly.

To aid the system 3301 in interacting with the different external datasources 3318, the dataset compensation model 3316 can include relevantinformation related to each external data source 3318 with which thesystem 3301 can interact. For example, the dataset compensation model3316 can include any one or any combination of: the amount of datastored in an external data source 3318, the type of data stored in anexternal data source, query commands supported by an external datasource (e.g., aggregation, filtering ordering), query translator totranslate a query into tasks supported by an external data source, thefile system type and hierarchy of the external data source 3318, numberof partitions supported by an external data source 3318, endpointlocations (e.g., location of processing nodes or processors), throttlingrequirements (e.g., number and rate at which requests can be sent to theexternal data source), etc.

The information about each external data source 3318 can be collected ina variety of ways. In some cases, some of the information about theexternal data source 3318 can be received when a customer sets up theexternal data source 3318 for use with the system 3301. For example, acustomer can indicate the type of external data source 3318 e.g., MySQL,PostgreSQL, and Oracle databases; NoSQL data stores like Cassandra,Mongo DB, cloud storage like Amazon S3 HDFS, etc. Based on thisinformation, the system 3301 can determine certain characteristics aboutthe external data store 3318, such as whether it supports multiplepartitions.

In addition, as discussed herein, different dataset sources havedifferent capabilities. For example, not only can different datasetssources support a different number of partitions, but the datasetsources can support different functions. For example, some datasetsources may be capable of data aggregation, filtering, or ordering,etc., while others may not be. The dataset compensation module 3316 canstore the capabilities of the different dataset sources to aid inproviding a seamless experience to users.

In certain cases, the system 3301 can collect relevant information aboutan external data source by communicating with it. For example, the querycoordinator 3304 or a worker node 3306 can interact with the externaldata source 3318 to determine the number of partitions available foraccessing data. In some cases, the number of available partitions maychange as computing resources on the external data source 3318 becomeavailable or unavailable, etc. In addition, when the system 3301accesses the external data source 3318 as part of a query it can trackrelevant information, such as the tables or amount of data accessed,tasks that the external data source was able to perform, etc. Similarly,the system 3301 can interact with an external data source 3318 toidentify the endpoint that will handle any subqueries and its location.The endpoint and endpoint location may change depending on the subquerythat is to be run on the external data source. Accordingly, in someembodiments, the system 3301 can request endpoint information with eachquery that is to access the particular external data source.

Using the information about the external data sources 3318, a querycoordinator 3304 can determine how to interact with it and how toprocess data obtained from the external data source 3318. For example,if an external data source 3318 supports parallel reads, the querycoordinator 3304 can allocate multiple partitions to read the data fromthe external data source 3318 in parallel. In some embodiments, thequery coordinator 3304 can allocate sufficient partitions or processors3406 to establish a 1:1 relationship with the available partitions atthe external data source 3318. Similarly, if the external data source3318 can perform some processing of the data, the query coordinator 3304can use the information from the dataset compensation module 3316 totranslate the query into commands understood by the external data source3318 and push some processing to the external data source 3318, therebyreducing the amount of system 3301 resources (e.g., nodes 3306) used toprocess the query.

Furthermore, in some cases, using the dataset compensation module 3316,the query coordinator can determine the amount of data in the differentexternal data sources that will be accessed by a particular query. Usingthat information, the query coordinator 3304 can intelligently interactwith the external data sources 3318. For example, if the querycoordinator 3304 determines that data with similar characteristics intwo external data sources are to be accessed and the data from each willeventually be combined, the query coordinator 3304 can first interactwith or query the external data source 3318 that includes less data andthen using information gleaned from that data prepare a more narrowlytailored query for the external data source 3318 with more data.

As a specific example, suppose a user wants to identify the source of aparticular error using information from an HDFS data source and anOracle data source, but does not know what the error is or whatgenerated it. To do so, the user enters a query that includes a requestto identify errors generated within a particular timeframe and stored inan HDFS data source and an Oracle data source and then correlate theerrors based on the error source. Based on the query, the querycoordinator 3304 determines that a union operation is to be performed onthe data from the HDFS data source and the Oracle data source based onthe source of the errors.

Additionally, suppose that the dataset compensation module 3316 hasidentified the HDFS data source as being relatively small and identifiedthe Oracle data source as being significantly larger than the HDFS datasource. Accordingly, based on the information in the datasetcompensation module 3316, the query coordinator 3304 can instruct thenodes 3306 to first intake and process the data from the HDFS datasource. Suppose that by doing so, the nodes 3306 determine that the HDFSdata source only includes fifty types of errors in the specifiedtimeframe from ten sources. Accordingly, using that information, thequery coordinator 3304 can instruct the nodes 3306 to limit the intakeof data from the Oracle data store based on the error type and/or thesource based on the error types and sources identified by firstanalyzing the HDFS data source.

As such, the query coordinator 3304 can reduce the amount of datarequested by the Oracle data store and the amount of processing neededto obtain the relevant result. For example, if the Oracle data storeincluded two hundred error types from one hundred sources, the querycoordinator 3304 avoided having to intake and process the data from allone hundred sources. Instead only the data from sources that matched theten sources from the HDFS data source were requested and processed bythe nodes 3306.

11.3. Query Coordinator

The query coordinator(s) 3304 can act as the primary coordinator orcontroller for queries that are assigned to it by the search head 210 orsearch process master 3302. As such, the query coordinator can process aquery, identify the resources to be used to execute the query, controland monitor the nodes to execute the query, process aggregate results ofthe query, and provide finalized results to the search head 210 orsearch process master 3302 for delivery to a client device 404.

11.3.1. Query Processing

Upon receipt of a query, the query coordinator 3304 can analyze thequery. In some cases analyzing the query can include verifying that thequery is semantically correct or performing other checks on the query todetermine whether it is executable by the system. In addition, the querycoordinator 3304 can analyze the query to identify the dataset sourcesthat are to be accessed and to define an executable search process. Forexample, the query coordinator 3304 can determine whether data from theindexers 206, external data sources 3318, query acceleration data store3308, or other dataset sources (e.g., common storage, ingested databuffers, etc.) are to be accessed to obtain the relevant datasets.

As part of defining the executable search process, the query coordinator3304 can identify the different entities that can perform someprocessing on the datasets. For example, the query coordinator 3304 candetermine what portion(s) of the query can be delegated to the indexers206, nodes 3306, and external data sources 3318, and what portions ofthe query can be executed by the query coordinator 3304, search processmaster 3302, or search head 210. For tasks that can be completed by theindexers 206, the query coordinator 3304 can generate task instructionsfor the indexers 206 to complete, as well as instructions to route allresults from the indexers 206 to the nodes 3306. For tasks that can becompleted by the external data sources 3318, the query coordinator 3304can use the dataset compensation module 3316 to generate taskinstructions for the external data sources 3318 and to determine how toset up the nodes 3306 to receive data from the external data sources3318.

In addition, as part of defining the executable search process, thequery coordinator 3304 can generate a logical directed acyclic graph(DAG) based on the query. FIG. 35 is a diagram illustrating anembodiment of a DAG 2000 generated as part of a search process. In theillustrated embodiment, the DAG 2000 includes seven vertices and sixedges, with each edge directed from one vertex to another, such that bystarting at any particular vertex and following a consistently-directedsequence of edges the DAG 2000 will not return to the same vertex.

Here, the DAG 2000 can correspond to a topological ordering of searchphases, or layers, performed by the nodes 3306. As such, a sequence ofthe vertices can represent a sequence of search phases such that eachedge is directed from earlier to later in the sequence of search phases.For example, the DAG 2000 may be defined based on a search string foreach phase or metadata associated with a search string. The metadata maybe indicative of an ordering of the search phases such as, for example,whether results of any search string depend on results of another searchstring such that the later search string must follow the former searchstring sequentially in the DAG 2000.

In the illustrated embodiment of FIG. 35 , the DAG 2000 can correspondto a query that identifies data from two dataset sources that are to becombined and then communicated to different locations. Accordingly, theDAG 2000 includes intake vertices 3502, 3508, a process vertex 3504,collect vertices 3506, 3510, a join vertex 3512, and a branch vertex3514.

Each vertex 3502, 3504, 3506, 3508, 3510, 3512, 3514 can correspond to asearch phase performed using one or more partitions or processors 3406of one or more nodes 3306 on a particular set of data. For example, theintake, process, and collect vertices 3502, 3504, 3506 can correspond todata search phases, or transformations, on data received from a firstdataset source. More specifically, the intake phase or vertex 3502 cancorrespond to one or more partitions that receive data from the firstdataset source, the process phase 3504 can correspond to one or morepartitions used to process the data received by the partitions at theintake phase 3502, and the collect phase 3506 can correspond to one ormore partitions that collect the results of the processing by thepartitions in the process phase 3504.

Similarly, the intake and collect vertices 3508, 3510 can correspond todata search phases performed using one or more partitions or processors3406 on data received from a second dataset source. For example, theintake phase 3508 can correspond to one or more partitions that receivedata from the second dataset source and the collect phase 3510 cancorrespond to one or more partitions that collect the results from thepartitions in the intake phase 3508.

The join and branch phases 3512, 3514 can correspond to data searchphases performed using one or more partitions or processors 3406 on datareceived from the different branches of the DAG 2000. For example, thejoin phase 3512 can correspond to one or more partitions used to combinethe data received from the partitions in the collect phases 3506, 3510.The branch phase 3514 can correspond to one or more partitions thatcommunicate results of the join phase 3512 to one or more destinations.For example, the partitions in the branch phase 3514 used to communicateresults of the query to the query coordinator 3304, an external datasource 3318, accelerated data source 3308, ingested data buffer, etc.

It will be understood that the number, order, and types of search phasesin the DAG 2000 can be determined based on the query. As a non-limitingexample, consider a query that indicates data is to be obtained fromcommon storage and an Oracle database, collated, and the results sent tothe query coordinator 3304 and an HDFS data store. In this example, inresponse to determining that the common storage do not provideprocessing capabilities, the query coordinator 3304 can generatevertices 3502, 3504, 3506 indicating that an intake phase 3502, processphase 3504, and collect phase 3506 will be used to process the data fromthe common storage sufficiently to be combined with data from the Oracledatabase. Similarly, based on a determination that the Oracle databasecan perform some processing capabilities, the query coordinator cangenerate vertices 3508, 3510 indicating that an intake phase 3508 andcollect phase 3510 will be used to sufficiently process the data fromthe Oracle database for combination with the data from the commonstorage.

The query coordinator 3304 can further generate the join phase 3512based on the query indicating that the data from the Oracle database andcommon storage is to be collated or otherwise combined (e.g., joined,unioned, etc.). In addition, based on the query indicating that theresults of the combination are to be communicated to the querycoordinator 3304 and the HDFS data store, the query coordinator 3304 cangenerate the branch phase 3514. As mentioned above, in each phase, thequery coordinator 3304 can allocate one or more partitions to performthe particular search phase.

It will be understood that the DAG 2000 is a non-limiting example of thesearch phases that can be included as part of a search process. In somecases, depending on the query, the DAG 2000 can include fewer or morephases of any type. For example, the DAG 2000 can include fewer or moreintake phases depending on the number of dataset sources. Additionally,depending on the particular processing requirements of a query, the DAG2000 can include multiple processing, collect, join, union, stats, orbranch phases, in any order.

In addition to determining the number and types of search phases for asearch process, the query coordinator 3304 can calculate the relativecost of each phase of the search process, determine the amount ofresources to allocate for each phase of the search process, generatetasks and instructions for particular nodes to be assigned to aparticular search process, generate instructions for dataset sources,generate tasks for itself and/or the search head 210, etc.

To calculate the relative cost of each phase of the search process anddetermine the amount of resources to allocate for each phase of thesearch process, the query coordinator 3304 can communicate with theworkload advisor 3310, workload catalog 3312, and/or the node monitor3314. As described previously, the workload advisor 3310 can use thedata collected in the workload catalog 3312 to determine the cost of aquery or an individual transformation or search phase of a searchprocess and to provide a resource allocation recommendation.Furthermore, the workload advisor 3310 can use the data from the nodemonitor module 3314 to determine the available resources in the system3301. Using this information, the query coordinator 3304 can determinethe cost for each search phase, the amount of resources available forallocation, and the amount of resources to allocate for each searchphase.

In determining the amount of resources to allocate for each searchphase, the query coordinator 3304 can also generate the tasks andinstructions for each node 3306. The instructions can include computerexecutable instructions that when executed by the node 3306 cause thenode 3306 to perform the task assigned to it by the query coordinator3304. For example, for nodes 3306 that are to be assigned to an intakephase 3502, 3508, the query coordinator 3304 can generate instructionson how to access a particular dataset source, what instructions are tobe sent to the dataset source, what to do with the data received fromthe dataset source, where do send the received data, how to perform anyload balancing or other tasks assigned to it, etc. For nodes 3306 thatare to process data in the process phase 3504, the query coordinator3304 can generate instructions indicating how to parse the receiveddata, relevant fields or keywords that are to be identified in the data,what to do with the identified field and keywords, where to send theresults of the processing, etc. Similarly, for nodes 3306 in the collectphases 3506, 3510, join phase 3512, or branch phase 3514, the querycoordinator 3304 can generate task instructions so that the nodes 3306are able to perform the task assigned to that particular phase. The taskinstructions can tell the nodes 3306 what data they are to process, howthey are to process the data, where they are to route the results of theprocessing, either between each other or to another destination. In somecases, the query coordinator 3304 can generate the tasks andinstructions for all nodes 3306 or processors 3406 and send theinstructions to all of the allocated nodes 3306 or processors 3406.Between them, the nodes 3306 or processors 3406 can determine or assignpartitions to be used to help execute the different instructions andtasks. The instructions sent to the nodes 3306 or processors 3406 caninclude additional parameters, such as a preference to use processors3406 partitions on the same machine for subsequent tasks. Suchinstructions can help reduce the amount of data communicated over thenetwork, etc.

In some embodiments, to generate instructions for the dataset sources,the query coordinator 3304 can use the dataset compensation module 3316.As described previously, the dataset compensation module 3316 caninclude relevant data about external data sources including, inter alia,processing abilities of the external dataset sources, number ofpartitions of the external dataset sources, instruction translators,etc. Using this information, the query coordinator 3304 can determinewhat processing to assign to the external data sources, and generateinstructions that will be understood by the external data sources. Inaddition, the query coordinator 3304 can have access to similarinformation about other dataset sources and/or communicate with thedataset sources to determine their processing capabilities and how tointeract with them (non-limiting examples: number of partitions to use,processing that can be pushed to the dataset source, etc.). Similarly,the query coordinator 3304 can determine how to interact with thedataset destinations so that the datasets can be properly sent to thecorrect location in a manner that the destination can store themcorrectly.

In some cases, the query coordinator 3304 can interact with onepartition of the external dataset source using multiple partitions. Forexample, the query coordinator 3304 can allocate multiple partitions tointeract with a single partition of the external dataset source. Thequery coordinator 3304 can break up a query or a subquery into multipleparts. Each part can be assigned to a different partition, which cancommunicate the subqueries to the partition of the external datasetsource. Thus, unbeknownst to the external dataset source, it canconcurrently process data from a single query.

Furthermore, the query coordinator 3304 can determine the order forconducting the search process. As mentioned above, in some embodiments,the query coordinator 3304 can determine that processing data from onedataset source could speed up the search process as a whole(non-limiting example: using data from one dataset source to generate amore targeted search of another dataset source). Accordingly, the querycoordinator 3304 can determine that one or more search phases are to becompleted first and then based on information obtained from the searchphase, additional search phases are to be initiated. Similarly, otheroptimizations can be determined by the query coordinator 3304. Suchoptimizations can include, but are not limited to, pushing processing tothe edges (e.g., to external data sources, etc.), identifying fields ina query that are key to the query and reducing processing based on theidentified field (e.g., if a relevant field is identified in a finalprocessing step, use the field to narrow the set of data that issearched for earlier in the search process), allocating the query tonodes that are physically close to each other or on the same machine,etc.

11.3.2. Query Execution and Node Control

Once the query is processed and the search scheme determined, the querycoordinator 3304 can initiate the query execution. In some cases, ininitiating the query, the query coordinator 3304 can communicate thegenerated task instructions to the various locations that will processthe data. For example, the query coordinator 3304 can communicate taskinstructions to the indexers 206, based on a determination that theindexers 206 are to perform some amount of processing on the dataset.Similarly, the query coordinator 3304 can communicate task instructionsto the nodes 3306, external data sources 3318, query acceleration datastore 3308, common storage, and/or ingested data buffer, etc.

In some embodiments, rather than communicating with the various datasetsources, the query coordinator 3304 can generate task instructions forthe nodes 3306 to interact with the dataset sources such that thedataset sources receive any task instructions from the nodes 3306 asopposed to the query coordinator 3304. For example, rather thancommunicating the task instructions directly to a dataset source, thequery coordinator 3304 can assign one or more nodes 3306 to communicatetask instructions to the external data sources 3318, indexers 206, orquery acceleration data store 3308. In certain embodiments, the querycoordinator 3304 can communicate the same search scheme or taskinstructions to the nodes 3306 or partitions of the nodes 3306 that havebeen allocated for the query. The allocated nodes 3306 or partitions ofthe nodes 3306 can then assign different groups to perform differentportions of the search scheme.

Upon receipt of the task instructions, the dataset sources and nodes3306 can begin operating in parallel. For example, if task instructionsare sent to the indexers 206 and to the nodes 3306, both can beginexecuting the instructions in parallel. In executing the taskinstructions, the nodes 3306 can organize their processors 3406 orpartitions according to task instructions. For example, some of thenodes 3306 can allocate one or more partitions or processors 3406 aspart of an intake phase, another partition as part of a processingphase, etc. In some cases, all partitions or processors 3406 of a node3306 can be allocated to the same task or to different tasks. In certainembodiments, it can be beneficial to allocate partitions from the samenode 3306 to different tasks reduce network traffic between nodes 3306or machines 3402.

FIG. 36 is a block diagram illustrating an embodiment of layers ofpartitions implementing various search phases of a query. In some cases,the layers can correspond to search phases in a DAG, such as the DAG2000 described in greater detail above. In the illustrated embodiment ofFIG. 36 , based on task instructions received from the query coordinator3304, the nodes 3306 have arranged various partitions to performdifferent search phases on data coming from a dataset source 3602. Asdescribed previously, the dataset source 3602 can correspond to indexers206, external data sources 3318, the query acceleration data store 3308,common storage, an ingested data buffer, or other source of data fromwhich the nodes 3306 can receive data.

As referenced in FIG. 35 , the partitions in each layer can interactwith the data based on task instructions received by the querycoordinator 3304. In the illustrated embodiment of FIG. 36 , thepartitions in the intake layer 3604 can receive the data from thedataset source 3602, which can be communicated to the partitions in theprocessing layer 3606 in a load-balanced fashion. The partitions in theprocessing layer 3606 can process the data based on the taskinstructions, which were generated based on the query, and the resultsprovided to the partitions in the collector layer 3608. Similarly, uponcompleting their assigned task, the processors associated with thepartitions in the collector layer 3608 can communicate the results oftheir processing to the branch layer 3610. In the illustrated embodimentof FIG. 36 , the branch layer 3610 communicates the results receivedfrom the partitions in the collector layer 3608 to a first datasetdestination 3614 and to partitions in a storage layer 3612 for storagein a second dataset destination 3616. It will be understood that feweror more layers can be included as desired, and can be based on thecontent of the particular query being executed. Furthermore, it will beunderstood that the layers can correspond to different map-reduceprocedures or commands. For example, as described herein, in theillustrated embodiments, the processing layer 3606 can correspond to amap procedure and the collector layer 3608 can correspond to a reduceprocedure. However, as described herein, it will be understand thatvarious layers can correspond to map or reduce procedures.

In the illustrated embodiment, four partitions have been allocated tothe intake layer 3604, eight partitions have been allocated to theprocessing layer 3606, five partitions have been allocated to thecollector layer 3608, one partition has been allocated to the branchlayer 3610, and three partitions have been allocated to the storagelayer 3612. However, it will be understood that fewer or more partitionscan be assigned to any layer as desired and fewer or additional layerscan be included. For example, based on a query that indicates multipledataset sources are to be accessed, the query coordinator 3304 canallocate separate intake, processing, and collector layers 3604, 3606,3608 for each dataset source 3602. Furthermore, based on the querycommands, the query coordinator can allocate additional layers, such asa join layer to combine data received from multiple dataset sources,etc.

In determining the number of partitions and/or processors 3406 for eachsearch phase or layer, the query coordinator 3304 can use the workloadadvisor 3310 and/or dataset compensation module 3316. For example, theworkload advisor 3310 can use historical data about executing individualsearch phases in queries to recommend an allocation scheme that providessufficient resources to process the query in a reasonable amount oftime.

In some cases, the query coordinator 3304 can allocate partitions forthe intake layer 3604 and storage layer 3612 based on information aboutthe number of partitions available for reading from the dataset source3602 and writing data to the dataset destination 3616, respectively. Thequery coordinator 3304 can obtain the information about the datasetsource 3602 or dataset destination 3616 from a number of locations,including, but not limited to, the workload catalog 3312, the datasetcompensation module 3316, or from the dataset source 3602 or datasetdestination 3616 itself. The information can inform the querycoordinator 3304 as to the number of partitions available for readingfrom the dataset source 3602 and writing to the dataset destination3616.

In some cases, the query coordinator 3304 can allocate partitions in theintake layer 3604 or the storage layer 3612 to have a one-to-one,one-to-many, or many-to-one correspondence with partitions in thedataset source 3602 or dataset destination 3616, respectively. Thecorrespondence between the partitions in the intake or storage layer3604, 3612 and the partitions in the dataset source or destination 3602,3616, respectively, can be based on a threshold number of partitions,the type of the dataset source/destination, etc.

In certain embodiments, if the query coordinator 3304 determines thatthe dataset source 3602 (or dataset destination 3616) has a number ofpartitions that satisfies a threshold number of partitions or determinesthat the number of partitions of the dataset source 3602 (or datasetdestination 3616) can be matched without overextending the nodes 3306,the query coordinator 3304 can allocate partitions in the intake layer3604 (or storage layer 3612) to have a one-to-one correspondence topartitions in the dataset source 3602 (or dataset destination 3616). Thenumber of partitions that satisfy the threshold number of partitions canbe determined based on the number of nodes 3306 or processors 3406 inthe system 3301, the number of available nodes 3306 in the system 3301,scheduled usage of nodes 3306, etc. Accordingly, the threshold number ofpartitions can be dynamic depending on the status of the processors3406, nodes 3306, or the system 3301. For example, if a large number ofnodes 3306 are available, the threshold number of nodes can be larger,whereas, if only a relatively small number of nodes 3306 are available,the threshold number can be smaller. Similarly, if the workload advisor33010 expects a large number of queries in the near term it can allocatefewer partitions to an individual query. Alternatively, if the workloadadvisor 33010 does not expect many queries in the near term it canallocate a greater number of partitions to an individual query.

In some cases, the query coordinator 3304 can determine whether to matchthe number of partitions in the dataset source 3602 or datasetdestination 3616 with corresponding partitions in the intake layer 3604or storage layer 3612, respectively, based on the type of the datasetsource 3602 or dataset destination 3616. For example, the querycoordinator 3304 can determine there should be a one-to-onecorrespondence of intake layer 3604 partitions to dataset source 3602partitions (or storage layer 3612 partitions to dataset destination 3616partitions) when the dataset source 3602 (or dataset destination 3616)is an external data source or ingested data buffer and that there shouldbe a one-to-multiple correspondence when the dataset source 3602 (ordataset destination 3616) is indexers 206, common storage, queryacceleration data store 3308, etc.

As a non-limiting example, if the dataset source 3602 is an externaldata source or ingested data buffer with four partitions and the querycoordinator 3304 determines that it can support a one-to-onecorrespondence, the query coordinator 3304 can allocate four partitionsto the intake layer 3604, as illustrated in FIG. 36 . Similarly, if thedataset destination 3616 is an external data source or ingested databuffer with three partitions and the query coordinator 3304 determinesthat it can support a one-to-one correspondence, the query coordinator3304 can allocate three partitions to the storage layer 3612, asillustrated in FIG. 36 . As another non-limiting example, if the datasetsource 3602 (or dataset destination 3616) is indexers 206, commonstorage, or query acceleration data stores 3308 with hundreds ofpotential partitions, and/or the query coordinator 3304 determines thatit cannot support a one-to-one correspondence, it can allocate the fourpartitions to the intake layer 3604 (or the three partitions to thestorage layer 3612), as illustrated in FIG. 36 .

In addition, during intake of the data from the dataset source 3602, thequery coordinator 3304 can dynamically adjust the number of partitionsin the intake layer 3604. For example, if an additional partition of thedataset source 3602 becomes available or one of the partitions becomesunavailable, the query coordinator 3304 can dynamically increase ordecrease the number of partitions in the intake layer 3604. Similarly,if the query coordinator 3304 determines that the intake layer 3604 istaking too much time and additional resources are available, it candynamically increase the number of partitions in the intake layer 3604.In addition, if the query coordinator 3304 determines that additionalresources are available or become unavailable, it can dynamicallyincrease or decrease the number of partitions in the intake layer 3604.Similarly, the query coordinator can dynamically adjust the number ofpartitions in the storage layer 3612.

Similar to the intake layer 3604 and storage layer 3612, the querycoordinator 3304 can allocate partitions to the different search layers3606, 3608, 3610 based on information about the query and information inthe workload catalog 3312. For example, the query may include requeststo process the data in a way that is resource intensive. As such, thequery coordinator 3304 can allocate a larger number of partitions and/orprocessors 3406 to the processing layer 3606 or use multiple processinglayers 3606 to process the data. In some cases, more partitions can beallocated to the search layers for queries of larger datasets.

In addition, during execution of the query, the query coordinator 3304can monitor the partitions in the search layers 3606, 3608, 3610 anddynamically adjust the number of partitions in each depending on thestatus of the individual partitions, the status of the nodes 3306, thestatus of the query, etc. In some cases, the query coordinator 3304 candetermine that a significant number of results are being sent to aparticular partition in the collector layer 3608. As such, the querycoordinator 3304 can allocate an additional partition to the collectorlayer and/or instruct that the results from the partitions in theprocessing layer 3606 be distributed in a different manner to reduce theload on the particular partition in the collector layer. In certaincases, the query coordinator 3304 can determine that a partition in theprocessing layer 3606 is not functioning or that there is significantlymore data coming from the dataset source 3602 than was anticipated.Accordingly, the query coordinator 3304 can allocate an additionalpartition 3606 to the processing layer. Conversely, if the querycoordinator 3304 determines that some of the partitions or processors3406 are underutilized, then it can deallocate it from a particularlayer and make it available for other queries, or assign it to adifferent layer, etc. Accordingly, the query coordinator 3304 candynamically allocate and deallocate resources to intake and process thedata from the dataset source 3602 in a time-efficient and performantmanner.

As a non-limiting example, consider a query that includes a request tocount the number of different types of errors in data stored in anexternal data source within a timeframe and to return the results to theuser and store the results in the query acceleration data store 3308.Based on the query, the query coordinator 3304 can generate a DAG thatincludes the intake layer 3604, processing layer 3606, collector layer3608, branch layer 3610, and storage layer 3612. Additionally, based ona determination that the external data source supports four partitions,the query coordinator 3304 allocates four partitions to the intake layer3604. In addition, based on the expected amount of data to be processed,the query coordinator 3304 allocates eight partitions to the processinglayer 3606, and five partitions to the collector layer 3608. Further,based on resource availability and the determination that the datasetdestination is the query acceleration data store 3308, which can supportmore than a threshold number of partitions, the query coordinator 3304allocates three partitions to the storage layer 3612. The taskinstructions for each partition of each search layer can be sent to thenodes 3306, which assign processors 3406 to the various tasks andpartitions. In some cases, the processors 3406 and partitions can have a1:1 correspondence, such that each partition corresponds to oneprocessor. In certain embodiments, multiple partitions can be assignedto a processor 3406 or vice versa. As such, when referred to herein as apartition performing an action, it will be understood that the actioncan be performed by the processor 3406 assigned to that partition.

During execution, the partitions (or processors assigned to thepartition) in the intake layer 3604 communicate with the dataset source3602 to receive the relevant data from the partitions of the datasetsource 3602. The data is then communicated to the partitions in theprocessing layer 3606. In the illustrated embodiment, each partition ofthe intake layer 3604 communicates data in a load-balanced fashion totwo partitions in the processing layer 3606. The partitions in theprocessing layer 3606 can parse the incoming data to identify eventsthat include an error and identify the type of error.

The partitions in the processing layer 3606 can determine the results tothe partitions in the collector layer 3608. For example, each partitionin the processing layer 3606 can apply a modulo five to the error typein order to attempt to equally separate the results between the fivepartitions in the collector layer 3608. As such, for each error type, apartition in the collector layer 3608 can include the total count oferrors for that type. Depending on the query, in some cases, thepartitions in the collector layer 3608 can also include the event thatincluded the particular error type.

The partitions in the collector layer 3608 can send the results to thepartition in the branch layer 3610. The partition in the branch layer3610 can communicate the results to the query coordinator 3304, whichcan communicate the results to the search head or client device. Inaddition, the branch layer 3610 can communicate the results to thepartitions in the storage layer 3612, which communicate the results inparallel to the query acceleration data store 3308.

Throughout the execution of the query, the query coordinator 3304 canmonitor the partitions in the intake layer 3604, processing layer 3606,collector layer 3608, branch layer 3610, and storage layer 3612. If onepartition becomes unavailable or becomes overloaded, the querycoordinator 3304 can allocate additional resources. Similarly, if apartitions is not being utilized, the query coordinator 3304 candeallocated it from a layer. For example, if a partition on the externaldata source becomes unavailable, a corresponding partition in the intakelayer 3604 may no longer receive any data. As such, the querycoordinator 3304 can deallocate that partition from the intake layer3604. In some embodiments, any change in state of a partition can bereported to the node monitor module 3314, which can be used by the querycoordinator to allocate resources.

11.3.3. Result Processing

Once the nodes 3306 have completed processing the query or particularresults of the query, they can communicate the results to the querycoordinator 3304. The query coordinator 3304 can perform any finalprocessing. For example, in some cases, the query coordinator 3304 cancollate the data from the nodes 3306. The query coordinator 3304 canalso send the results to the search head 210 or to a datasetdestination. For example, based on a command (non-limiting example“into”), the query coordinator 210 can store results in the queryacceleration data store 3308, an external data source 3318, an ingesteddata buffer, etc. In addition, the query coordinator 3304 cancommunicate to the search process master 3302 that the query has beencompleted. In the event all queries assigned to the query coordinator3304 have been completed, the query coordinator can shut down or enter ahibernation state and await additional queries assigned to it by thesearch process master 3302.

11.4. Query Acceleration Data Store

As described herein, a query can indicate that information is to bestored (e.g., stored in non-volatile or volatile memory) in the queryacceleration data store 3308.

As described above, the query acceleration data store 3308 can storeinformation (e.g., datasets) sourced from other dataset sources, suchas, external data sources 3318, indexers 206, ingested data buffers,indexers, and so on. For example, when providing a query, a user canindicate that particular information is to be stored in the queryacceleration data source 3308 (e.g., cached). The information caninclude the results of the query, partial results of the query, data(processed or unprocessed) received from another dataset source via thenodes 3306, etc. Subsequently, the data intake and query system 3301 cancause queries directed to the particular information to utilize thequery acceleration data store 3308. In this way, the stored informationcan be rapidly accessed and utilized.

As an example, the query can indicate that information is to be obtainedfrom the external data sources 3318. Since the external data sources3318 may have potentially high latency, response times to particularqueries, the query can be constrained according to characteristics ofthe external data sources 3318. For example, particular external datasources 3318 may be limited in their processing speed, networkbandwidth, and so on, such that the worker nodes 3306 are required towait longer for information. As described herein, the query cantherefore specify that particular information from the external datasources 3318 (or other dataset sources) be stored in the queryacceleration data store 3308. Subsequent queries that utilize thisparticular information can then be executed more quickly. For example,in subsequent queries the worker nodes 3306 can obtain the particularinformation from the query acceleration data store 3308 rather than fromthe external data source 3318.

An example query can be of a particular form, such as:

Query=<from [dataset source]>|<[logic]>|[accelerated directive]

In the above example, the query indicates that information is to beobtained from a dataset source, such as an external data source 3318.Optionally, the query can indicate particular tables, documents,records, structured or unstructured information, and so on. As describedabove, the data intake and query system 3301 can process the query anddetermine that the external data source is being referenced. The nextelement of the query (e.g., a request parameter) includes logic to beapplied to the data from the external data source, for example the logiccan be implemented as structured query language (SQL), search processinglanguage (SPL), and so on. As described above, the worker nodes 3306 canobtain the requested data, and apply the logic to obtain information tobe provided in response to the query.

In the above example query, an accelerated directive is included. Forexample, the accelerated directive can be a particular term (e.g., “intoquery acceleration data store”), symbol, and so on, included in thequery. The accelerated directive can optionally be manually included inthe query (e.g., a user can type the directive), or automatically. As anexample of automatically including the directive, a user can indicate ina user interface associated with entering queries that information is tobe stored in the query acceleration data store 3308. As another example,the user's client device or query coordinator 3304 can determine thatinformation is to be stored in the data store 3308. For example, thequery can be analyzed by the client device or query coordinator 3304,and based on a quantity of information being requested, the clientdevice or query coordinator 3304 can automatically include theaccelerated directive (e.g., if greater than a threshold quantity isbeing requested, the directive can be included). Optionally, the dataintake and query system 3301 can automatically store the requestedinformation in the query acceleration data store 3308 without anaccelerated directive in a received query. For example, the query system3301 can automatically store data in the query acceleration data store3308 based on a user ID (e.g., always store results for a particularuser or based on recent use by the user), time of day (e.g., storeresults for queries made at the beginning or end of a work day, etc.),dataset source identity (e.g., store data from dataset source identifiedhas having a slower response time, etc.), network topology (e.g., storedata from sources on a particular network given the network bandwidth,etc.) etc. Although the above example shows the accelerated directive atthe end of the query, it will be understood that it can be placed at anypart of it. In some cases, the result of the command preceding theaccelerated directive corresponds to the data stored in the queryacceleration data store 3308.

Upon receipt of the query, the data intake and query system 3301 (e.g.,the query coordinator 3304) can cause the requested information from thedataset source to be stored in the query acceleration data store 3308.Optionally, the query acceleration data store 3308 can receive theprocessed result associated with the query (e.g., from the worker nodes3306). The query acceleration data store 3308 can then provide theprocessed result to the query coordinator 3304 to be relayed to therequesting client. However, to increase response times, the worker nodes3306 can provide processed information to the query acceleration datastore 3308, and also to the query coordinator 3304. In this way, thequery acceleration data store 3308 can store (e.g., in low latencymemory, or longer latency memory such as solid state storage or diskstorage) the received processed information, while the query coordinator3304 can relay the received processed information to the requestingclient.

The processed result may be stored by the query acceleration data store3308 in association with an identifier, such that the information can beeasily referenced. For example, the query acceleration data store 3308can generate a unique identifier upon receipt of information for storageby the worker nodes 3306. For subsequent queries, the query coordinator3304 can receive the identifier, such that the query coordinator 3304can replace the initial portion with the unique identifier.

In some embodiments, the query coordinator 3304 can generate the uniqueidentifier. For example, the query coordinator can receive informationfrom the query acceleration data store 3308 indicating that it storedinformation. The query coordinator 3304 can maintain a mapping betweengenerated unique identifiers and datasets, partitions, and so on, thatare associated with information stored by the query acceleration datastore 3308. The query coordinator 3304 may optionally provide a uniqueidentifier to the requesting client, such that a user of the requestingclient can re-use the unique identifier. For example, the user's clientcan present a list of all such identifiers along with respective queriesthat are associated with the identifier. The user can select anidentifier, and generate a new query that is based on an associatedquery.

In addition to storing the data or the results or partial results of thequery, the query acceleration data store can store additionalinformation regarding the results. For example, the query accelerationdata store can store information about the size of the dataset, thequery that resulted in the dataset, the dataset source of the dataset,the time of the query that resulted in the dataset, the time range ofdata that was processed to produce the dataset, etc. This informationcan be used by the system 3301 to prompt a user as to what data isstored and can be used in the query acceleration data store, determinewhether portions of an incoming query correspond to datasets in theaccelerate data store, etc. This information can also be stored in theworkload catalog 3312, or otherwise made available to the querycoordinator 3304.

Subsequently, for received queries that reference the processedinformation, the query coordinator 3304 can cause the worker nodes 3306to obtain the information from the query acceleration data store 3308.

For example, a subsequent query can be

Query=<from [dataset source]>|<[logic]>|<subsequent logic>

In the above query, the query coordinator 3304 can determine that someportion of the data referenced in the query corresponds to data that isstored in the query acceleration data store 3308 (previously storeddata) or was previously processed according to a prior query (e.g., thequery represented above) and the results of the processing stored in thequery acceleration data store 3308. For example, the query coordinator3304 can compare the query to prior queries, and any portion of datathat was referenced in a prior query. The query coordinator 3304 canthen instruct the worker nodes 3306 to obtain the previously stored dataor the results of processing the data from the query acceleration datastore 3308. In some cases, the subsequent query can include an explicitcommand to obtain the data or results from the query acceleration datastore 3308.

Obtaining the previously stored data or results of processing the dataprovides multiple technical advantages. For example, the worker nodes3306 can avoid having to reprocess the data, and instead can utilize theprior processed result. Additionally, the worker nodes 3306 can morerapidly obtain information from the query acceleration data store 3308than, for example, the external data sources 3318. As an example, theworker nodes 3306 may be in communication with the query accelerationdata store 3308 via a direct connection (e.g., virtual networks, localarea networks, wide area networks). In contrast, the worker nodes 3306may be in communication with the external data sources 3318 via a globalnetwork (e.g., the internet).

As a non-limiting example, in some cases, a first query can indicatethat data from a dataset source is to be stored in the queryacceleration data store 3308 with minimal processing by the nodes 3306or without transforming the data from the dataset source. A subsequentquery can indicate that the data stored in the query acceleration datastore 3308 is to be processed or transformed, or combined with otherdata or results to obtain a result. In certain cases, the first querycan indicate that data from the dataset source is to be transformed andthe results stored in the query acceleration data store 3308. Thesubsequent query can indicate that the results stored in the queryacceleration data store 3308 are to be further processed, combined withdata or results from another dataset source, or provided to a clientdevice.

Furthermore, in certain embodiments, the worker nodes 3306 can performany additional processing on the results obtained from the queryacceleration data store 3308, while concurrently obtaining data fromanother dataset source and processing it to obtain additional results.In some cases, the results stored in the query acceleration data store3308 can be communicated to a client device while the nodes concurrentlyobtain data from another dataset source and process it to obtainadditional results. By obtaining, processing, and displaying the resultsof the previously processed data while concurrently obtaining additionaldata to be processed, processing the additional data, and communicatingthe results of processing the additional data, the system 3301 canprovide a more effective responsiveness to a user and decrease theresponse time of a query.

For the subsequent query identified above, the ‘subsequent logic’ can beapplied by the worker nodes 3306 based on the processed result stored bythe query acceleration data store 3308. The result of the subsequentquery can then be provided to the query coordinator 3304 to be relayedto the requesting client.

The query acceleration data store 3308, as described herein, canmaintain information in low-latency memory (e.g., random access memory)or longer-latency memory. That is, the query acceleration data store3308 can cause particular information to spill to disk when needed,ensuring that the data store 3308 can service large amounts of queries.Since, in some implementations, the low-latency memory can be less thanthe longer-latency memory, the query acceleration data store 3308 candetermine which datasets are to be stored in the low-latency memory. Insome embodiments, to provide this functionality, the query accelerationdata store 3308 can be implemented as a distributed in-memory data storewith spillover to disk capabilities. For example, the data in the queryacceleration data store 3308 can be stored in low-latency volatilememory, and in the event, the capacity of the low-latency volatilememory is reached, the data can be stored to disk.

In some embodiments, the query acceleration data store 3308 can utilizeone or more storage policies to swap datasets between low-latency memoryand longer-latency memory. Additionally, the query acceleration datastore 3308 can flush particular datasets after determining that thedatasets are no longer needed (e.g., the user can indicate that thedatasets can be flushed, or a threshold amount of time can pass).

As an example of a storage policy, the query acceleration data store3308 can store a portion of a dataset in low-latency memory whilestoring a remaining portion in longer-latency memory. In this way, thequery acceleration data store 3308 can have faster access to at least aportion each user's dataset. If a subsequent query is received by thedata intake and query system 3301 that references a stored dataset, thequery acceleration data store 3308 can access the portion of the storeddataset that is in low-latency memory. Since this access is, in general,with low-latency, the query acceleration data store 3308 can quicklyprovide this information to the worker nodes 3306 for processing. At asame, or similar, time, the query acceleration data store 3308 canaccess the longer-latency memory and obtain a remaining portion of thestored dataset. The worker nodes 3306 can then receive this remainingportion for processing. Therefore, the worker nodes 3306 can quicklyrespond to a request, based on the initially received portion from thelow-latency memory. In this way, the user can receive search results ina manner that appears to be in ‘real-time’, that is, the search resultscan be provided in a less than a threshold amount of time (e.g., 1second, 5 seconds, 10 seconds). Subsequent search results can then beprovided upon the worker nodes 3306 processing the portion from thelonger-latency memory.

The above-described storage policy may be based on a size of thedataset(s). For example, an example dataset may be less than athreshold, and the query acceleration data store 3308 may store theentirety of the dataset in low-latency memory. For an example datasetgreater than the threshold, the data store 3308 may store a portion inlow-latency memory. As the size of the dataset increases, the queryacceleration data store 3308 can store an increasingly lesser sizedportion in low-latency memory. In this way, the data store 3308 canensure that large data sets do not consume the low-latency memory.

While the queries described above indicate, a first query that includesan accelerated directive, and a second query that includes the firstquery (e.g., as an initial portion), optionally the data intake andquery system 3301 can receive a first query that is a combination of thefirst query and second query described above. For example, an exampleinitial query can be

Query=<from [dataset source]>|<[logic]>|[accelerateddirective]|<subsequent logic>

The above example query indicates that the data intake and query system3301 is to obtain information from an example dataset source (e.g.,external data source 3318), process the information, and cause the queryacceleration data store 3308 to store the processed information. Inaddition, subsequent logic is to be applied to the processedinformation, and the result provided to the requesting client 404 a-404n.

FIG. 36 illustrates a branch layer 3610, which for the example querydescribed above, can be utilized to provide information both to thequery acceleration data store 3308 and the data destination 3614 (e.g.,the requesting client). For example, subsequent to the worker nodes 3306obtaining processed information (e.g., based on the dataset source andlogic), the worker nodes 3306 can provide the processed information forstorage in the query acceleration data store 3308 while continuing toprocess the query (e.g., apply the subsequent logic). That is, theworker nodes 3306 can bifurcate the data (e.g., at branch layer 3610),such that the query acceleration data store 3308 can store partialresults while the worker nodes 3306 service the query and provide thecompleted results to the query coordinator 3304. Optionally, anotherquery may be received that references the partial results in the datastore 3308, and one or more worker nodes 3306 may access the data store3308 to service the other query. For example, the other query may beprocessed at a same time as the above-described example initial query.

Received queries can further indicate multiple datasets stored by thequery acceleration data store 3308. For example, a first query canindicate that first information is to be obtained (e.g., from externaldata source 3318, indexers 206, common storage, and so on) and stored inthe query acceleration data store 3308 as a first dataset. Additionally,a second query can indicate that second information is to obtained andstored in the data store 3308 as a second dataset. Subsequent queriescan then reference the stored first dataset and second dataset, suchthat logic can be applied to both the first and second dataset via rapidaccess to the query acceleration data store 3308.

Furthermore, queries can reference datasets stored by the queryacceleration data store 3308, and also datasets to be obtained fromanother dataset source (e.g., from external data source 3318, indexers206, ingested data buffer, and so on). For particular queries, the dataintake and query system 3301 may be able to provide results (e.g.,search results) from the query acceleration data store 3308 whiledatasets is being obtained from another dataset source. Similarly, thesystem 3301 may be able to provide results from the data store 3308while data obtained from another dataset source is being processed.

As an example, a first query can cause a dataset to be stored in thequery acceleration data store 3308, with the dataset being from anexternal data source 3318 and representing records from a prior timeperiod (e.g., one hour). Subsequently, a second query can reference thestored dataset and further cause newer records to be obtained from theexternal data source (e.g., a subsequent hour). For this second query,particular logic indicated in the second query can enable the dataintake and query system 3301 to provide results to a requesting clientbased on the stored dataset in the query acceleration data store 3308.As an example, the second query can indicate that the system 3301 is tosearch for a particular name. The worker nodes 3306 can obtain storedinformation from the query acceleration data store 3308, and identifyinstances of the particular name.

This access to the query acceleration data store 3308, as describedabove, can be low-latency. For example, the query acceleration datastore 3308 may have a portion of the stored information in low-latencymemory, such as RAM or volatile memory, and the worker nodes 3306 canquickly obtain the information and identify instances of the particularname. These identified instances can then be relayed to the requestingclient. Similarly, the query acceleration data store 3308 may have adifferent portion of the stored information in longer-latency memory,and can similarly identify instances of the particular name to beprovided to the requesting client.

The above-described worker node 3306 interactions with the queryacceleration data store 3308 can occur while information is beingobtained, or processed, from the external data source 3318 referenced bythe second query. In this way, the requesting client can view searchresults, for example search results based on the dataset stored by thequery acceleration data store 3308, while subsequent search results arebeing determined (e.g., search results based on information from adifferent dataset source). Furthermore, and as described above, thedataset being obtained from the other dataset source can be provided tothe query acceleration data store 3308 for storage, for example,provided while the worker nodes 3306 apply logic to determine resultsfrom the obtained dataset.

To increase security of the datasets stored by the query accelerationdata store, access controls can be implemented. For example, eachdataset can be associated with an access control list, and the querycoordinator 3304 can provide an identification of a requesting user tothe worker nodes 3306 and/or query acceleration data store 3308. Forexample, the identification can be an authorization or authenticationtoken associated with the user. The query acceleration data store 3308can then ensure that only authorized users are allowed access to storeddatasets. For example, a user who causes a dataset to be stored in thequery acceleration data store 3308 (e.g., based on a provided query) canbe indicated as being authorized (e.g., in an access control listassociated with the dataset). Optionally, the user can indicate one ormore other users as having access. Optionally, the data intake and querysystem 3301 can utilize role-based access controls to allow any userassociated with a particular role to access particular datasets. In thisway, the stored information can be secure while enabling the queryacceleration data store 3308 to service multitudes of users.

12.0. Query Data Flow

FIG. 37 is a data flow diagram illustrating an embodiment ofcommunications between various components within the environment 3300 toprocess and execute a query. At (1), the search head 210 receives andprocesses a query. At (2), the search head 210 communicates the query tothe search process service 2202, which can refer to the search processmaster 3302 and/or query coordinator 3304.

At (3) the search process service processes the query. As described ingreater detail above, as part of processing the query, the querycoordinator 3304 can identify the dataset sources (e.g., external datasources 3318, indexers 206, query acceleration data store 3308, commonstorage, ingested data buffer, etc.) to be accessed, generateinstructions for the dataset sources based on their processingcapabilities or communication protocols, determine the size of thequery, determine the amount of resources to allocate for the query,generate instructions for the nodes 3306 to execute the query, andgenerate tasks for itself to process results from the nodes 3306.

At (4), the query coordinator 3304 communicates the task instructionsfor the query to the worker nodes 3306 and/or the dataset sources 2202.As described above, in some embodiments, the query coordinator 3304 cancommunicate task instructions to the dataset sources 2202. In certainembodiments, the nodes 3306 communicate task instructions to the datasetsources 2202.

At (5), the nodes 3306 and/or dataset sources 2202 process the receivedinstructions. As described in greater detail above, the instructions forthe dataset sources 2202 can include instructions for performing certaintransformations on the data prior to communicating the data to the nodes3306, etc. As described in greater detail above, the instructions forthe nodes 3306 can include instructions on how to access the relevantdata, the number of search phases or layers to be generated, the numberof partitions to be allocated for each search phase or layer, the tasksfor the partitions in the different layer, data routing information toroute data between the nodes 3306 and to the search process service2202, etc. As such, based on the received instructions, the nodes 3306can assign partitions to different layer and begin executing the taskinstructions.

At (6), the nodes 3306 receive the data from the dataset source(s). Asdescribed in greater detail above, the nodes 3306 can receive the datafrom one or more dataset sources 2202 in parallel. In addition, thenodes 3306 can receive the data from a dataset source using one or morepartitions. The data received from the dataset sources 2202 can besemi-processed data based on the processing capabilities of the datasetsource 2204 or it can be unprocessed data from the dataset source 2204.

At (7), the nodes 3306 process the data based on the task instructionsreceived from the query coordinator 3304. As described in greater detailabove, the nodes can process the data using one or more layers, eachhaving one or more partitions assigned thereto. Although not illustratedin FIG. 37 , it will be understood that the search process service 2202can monitor the nodes 3306 and dynamically allocate resources based onthe monitoring.

At (8), the nodes 3306 communicate the results of the processing to thequery coordinator 3304 and/or to a dataset destination 2204. In somecases the dataset destination 2204 can be the same as the datasetsource. For example, the nodes 3306 can obtain data from the ingesteddata buffer and then return the results of the processing to a differentsection of the ingested data buffer, or obtain data from the queryacceleration data store 3308 or an external data source 3318 and thenreturn the results of the processing to the query acceleration datastore 3308 or external data source 3318, respectively. However, incertain embodiments, the dataset destination 2204 can be different fromthe dataset source 2204. For example, the nodes 3306 can obtain datafrom the ingested data buffer and then return the results of theprocessing to the query acceleration data store 3308 or an external datasource 3318.

At (9), the search process service 2202 can perform additionalprocessing, and at (10) the results can be communicated to the searchhead 210 for communication to the client device. In some cases, prior tocommunicating the results to the client device, the search head 210 canperform additional processing on the results.

It will be understood that the query data flow can include fewer or moresteps. For example, in some cases, the search process service 2202 doesnot perform any further processing on the results and can simply forwardthe results to the search head 210. In certain embodiments, nodes 3306receive data from multiple dataset sources 2204, etc.

13.0. Query Coordinator Flow

FIG. 38 is a flow diagram illustrative of an embodiment of a routine3800 implemented by the query coordinator 3304 to provide query results.Although described as being implemented by the query coordinator 3304,one skilled in the relevant art will appreciate that the elementsoutlined for routine 3800 can be implemented by one or more computingdevices/components that are associated with the system 3301, such as thesearch head 210, search process master 3301, indexer 206, and/or workernodes 3306. Thus, the following illustrative embodiment should not beconstrued as limiting.

At block 3802, the query coordinator 3304 receives a query. As describedin greater detail above, the query coordinator 3304 can receive thequery from the search head 210, search process master 3302, etc. In somecases, the query coordinator 3304 can receive the query from a client404. The query can be in a query language as described in greater detailabove. In some cases, the query received by the query coordinator 3304can correspond to a query received and reviewed by the search head 210.For example, the search head 210 can determine whether the query wassubmitted by an authenticated user and/or review the query to determinethat it is in a proper format for the data intake and query system 3301,has correct semantics and syntax, etc. In some cases, the search head210 can run a daemon to receive search queries, and in some cases, spawna search process, to communicate the received query to and receive theresults from the query coordinator 3304 or search process master 3302

At block 3804, the query coordinator 3304 processes the query. Asdescribed in greater detail above and as will be described in greaterdetail in FIG. 39 , processing the query can include any one or anycombination of: identifying relevant dataset sources and destinationsfor the query, obtaining information about the dataset sources anddestinations, determining processing tasks to execute the query,determining available resources for the query, and/or generating a queryprocessing scheme to execute the query based on the information. In someembodiments, as part of generating a query processing scheme, the querycoordinator 3304 allocates multiple layers or search phases ofpartitions to execute the query. Each level of partitions can be given adifferent task in order to execute the query. For example, as describedin greater detail above with reference to FIGS. 20 and 21 , one levelcan be given the task of interacting with the dataset source andreceiving data from the dataset source, another level can be tasked withprocessing the data received from the dataset source, a third level canbe tasked with collecting results of processing the data, and additionallevels can be tasked with communicating results to differentdestinations, storing the results in one or more dataset destinations,etc. The query coordinator 3304 can allocate as many or as few levels ofpartitions to execute the query.

At block 3806, the query coordinator 3304 distributes the query forexecution. Distributing the query for execution can include any one orany combination of: communicating the query processing scheme to thenodes 3306, monitoring the nodes 3306 during the processing of thequery, or allocating/deallocating resources based on the status of thenodes and the query, and so forth, described in greater herein.

At block 3808, the query coordinator 3304 receives the results. In someembodiments, the query coordinator 3304 receives the results from thenodes 3306. For example, upon completing the query processing scheme, oras a part of it, the nodes 3306 can communicate the results of the queryto the query coordinator 3304. In certain cases, the query coordinator3304 receives the results from the query acceleration data store, orindexers 206, etc. In some cases, the query coordinator 3304 receivesthe results from one or more components of the data intake and querysystem 3301 depending on the dataset sources used in the query.

At block 3810, the query coordinator 3304 processes the results. Asdescribed in greater detail above, in some cases, the results of a querycannot be finalized by the nodes 3306. For example, in some cases, allof the data must be gathered before the results can be determined. As anon-limiting example, for some cursored searches, the query coordinator3304, a result cannot be determined until all relevant data has beencollected by the worker nodes. In such cases, the query coordinator 3304can receive the results from the worker nodes 3306, and then collate theresults.

At block 3812, the query coordinator 3304 communicates the results. Insome embodiments, the query coordinator 3304 communicates the results tothe search head 210, such as a search process generated by the search tohandle the query. In certain cases, the query coordinator 3304communicates the results to the search process master 3302 or clientdevice 404, etc.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 3800. In some cases, one or more blocks can beomitted. For example, in certain embodiments, the results received fromnodes 3306 can be in a form that does not require any additionalprocessing by the query coordinator 3304. In such embodiments, the querycoordinator 3304 can communicate the results without additionalprocessing. As another example, the routine 3800 can include monitoringnodes during execution of the query or query processing scheme,allocating or deallocating resources during the execution of the query,etc. Similarly, routine 3800 can include reporting completion of thequery to a component, such as the search process master 3302, etc.

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 38 can be implemented in a variety oforders. In some cases, the query coordinator 3304 can implement someblocks concurrently or change the order as desired. For example, thequery coordinator 3304 can receive (3808), process (3810), and/orcommunicate results (3812) concurrently or in any order, as desired.

14.0. Query Processing Flow

FIG. 39 is a flow diagram illustrative of an embodiment of a routine3900 implemented by the query coordinator 3304 to process a query.Although described as being implemented by the query coordinator 3304,one skilled in the relevant art will appreciate that the elementsoutlined for routine 3900 can be implemented by one or more computingdevices/components that are associated with the system 3301, such as thesearch head 210, search process master 3301, indexer 206, and/or workernodes 3306. Thus, the following illustrative embodiment should not beconstrued as limiting.

At block 3902, the query coordinator 3304 identifies dataset sourcesand/or destinations for the query. In some cases, the query explicitlyidentifies the dataset sources and destinations that are to be used inthe query. For example, the query can include a command indicating thatdata is to be retrieved from the query acceleration data store 3308,ingested data buffer, common storage, indexers, or an external datasource. In certain cases, the query coordinator 3304 parses the query toidentify the dataset sources and destinations that are to be used in thequery. For example, the query may identify the name (or otheridentifier) of the location (e.g., my index) of the relevant data andthe query coordinator 3304 can use the name or identifier to determinewhether that particular location is associated with the queryacceleration data store 3308, ingested data buffer, common storage,indexers 206, or an external data source 3318.

In some cases, the query coordinator identifies the dataset source basedon timing requirements of the search. For example, in some cases,queries for data that satisfy a timing threshold or are within a timeperiod are handled by indexers or correspond to data in an ingested databuffer, as described herein. In some embodiments, data that does notsatisfy the timing threshold or is outside of the time period are storedin common storage, query acceleration data stores, external datasources, or by indexers. For example, as described in greater detailherein, in some cases, the indexers fill hot buckets with incoming data.Once a hot bucket is filled, it is stored. In some embodiments hotbuckets are searchable and in other embodiments hot buckets are not.Accordingly, in embodiments where hot buckets are searchable, a querythat reflects a time period that includes hot buckets can indicate thatthe dataset source is the indexers, or hot buckets being processed bythe indexers. Similarly, in embodiments where warm buckets are stored bythe indexers, a query that reflects a time period that includes warmbuckets can indicate that the dataset source is the indexers.

In certain embodiments, a query for data that satisfies the timingthreshold or is within the time period can indicate that the ingesteddata buffer is the dataset source. Further, in embodiments, where warmbuckets are stored in a common storage, a query for data that does notsatisfy the timing threshold or is outside of the time period canindicate that the common storage is the dataset source. In someembodiments, the time period can be reflective of the time it takes fordata to be processed by the data intake and query system 3301 and storedin a warm bucket. Thus, a query for data within the time period canindicate that the data has not yet been indexed and stored by theindexers 206 or that the data resides in hot buckets that are stillbeing processed by the indexers 206.

In some embodiments, the query coordinator 3304 identifies the datasetsource based on the architecture of the system 3301. As describedherein, in some architectures, real-time searches or searches for datathat satisfy the timing threshold are handled by indexers. In otherarchitectures, these same types of searches are handled by the nodes3306 in combination with the ingested data buffer. Similarly, in certainarchitectures, historical searches, or searches for data that do notsatisfy the timing threshold are handled by the indexers. In otherarchitectures, these same types of searches are handled by the nodes3306 in combination with the common storage.

At block 3904 the query coordinator 3304 obtains relevant informationabout the dataset sources/destinations. The query coordinator 3304 canobtain the relevant information from a variety of sources, such as theworkload advisor 3310, workload catalog 3312, dataset compensationmodule 3316, the dataset sources/destinations themselves, etc. Forexample, if the dataset source/destination is an external data source,the query coordinator 3304 can obtain relevant information about theexternal dataset source 3318 from the dataset compensation module or bycommunicating with the external data source 3318. Similarly, if thedataset source/destination is an indexer 206, common storage, queryacceleration data store 3308, ingested data buffer, etc., the querycoordinator can obtain relevant information by communicating with thedataset source/destination and/or the workload advisor 3310 or workloadcatalog 3312.

The relevant information can include, but is not limited to, informationto enable the query coordinator 3304 to generate a search scheme withsufficient information to interact with and obtain data from a datasetsource or send data to a dataset destination. For example, the relevantinformation can include information related to the number of partitionssupported by the dataset source/destination, location of compute nodesat the dataset source/destination, computing functionality of thedataset source/destination, commands supported by the datasetsource/destination, physical location of the dataset source/destination,network speed and reliability in communicating with the datasetsource/destination, amount of information stored by the datasetsource/destination, computer language or protocols for communicatingwith the dataset source/destination, summaries or indexes of data storedby the dataset source/destination, data format of data stored by thedataset source/destination, etc.

At block 3906, the query coordinator 3304 determines processingrequirement for the query. In some cases, to determine the processingrequirements, the query coordinator 3304 parses the query. As describedpreviously, the workload catalog 3312 can store information regardingthe various transformations or commands that can be executed on data andthe amount of processing to perform the transformation or command. Insome cases, this information can be based on historical information fromprevious queries executed by the system 3301. For example, the querycoordinator 3304 can determine that a “join” command will havesignificant computational requirements, whereas a “count by” command maynot. Using the information about the transformations included in thequery, the query coordinator can determine the processing requirementsof individual transformations on the data, as well as the processingrequirements of the query.

At block 3908, the query coordinator 3304 determines availableresources. As described in greater detail above, the nodes 3306 caninclude monitoring modules that monitor the performance and utilizationof its processors. In some cases, a monitoring module can be assignedfor each processor on a node. The information about the utilization rateand other scheduling information can be used by the query coordinator3304 to determine the amount of resources available for the query.

At block 3910, the query coordinator 3304 generates a query processingscheme. In some cases, the query coordinator 3304 can use theinformation regarding the dataset sources/destinations, the processingrequirements of the query and/or the available resources to generate thequery processing scheme. As part of generating the query processingscheme, the query coordinator 3304 can generate instructions to beexecuted by the dataset sources/destinations, allocatepartitions/processors for the query, generate instructions for thepartitions/nodes, generate instructions for itself, generate a DAG, etc.

As described in greater detail above, in some embodiments, to generateinstructions for the dataset sources/destinations, the query coordinator3304 can use the information from the dataset compensation module 3316.This information can be used by the query coordinator 3304 to determinewhat processing can be done by an external data source, how to translatethe commands or subqueries for execution to the external dataset source,the number of partitions that can be used to read data from the externaldataset source, etc. Similarly, the query coordinator 3304 can generateinstructions for other dataset sources, such as the indexers, queryacceleration data store, common storage, etc. For example, the querycoordinator 3304 can generate instructions for the ingested data bufferto retain data until it receives an acknowledgment from the querycoordinator that the data from the ingested data buffer has beenreceived and processed.

In addition, as described in greater detail above, to generateinstructions for the processors/partitions, the query coordinator 3304can determine how to break up the processing requirements of the queryinto discrete or individual tasks, determine the number ofpartitions/processors to execute the task, etc. In some cases, thedetermine how to break up the processing requirements of the query intodiscrete or individual tasks, the query coordinator 3304 can parse thequery to its different portions of the query and then determine thetasks to use to execute the different portions.

The query coordinator 3304 can then use this information to generatespecific instructions for the nodes that enable the nodes to execute theindividual tasks, route the results of each task to the next location,and route the results of the query to the proper destination. Theinstructions for the nodes can further include instructions forinteracting with the dataset sources/destinations. In some cases,instructions for the dataset sources can be embedded in the instructionsfor the nodes so that the nodes can communicate the instructions to thedataset sources/destinations. Accordingly, the instructions generated bythe query coordinator 3304 for the nodes can include all of theinformation in order to enable the nodes to handle the various tasks ofthe query and provide the query coordinator with the appropriate data sothat the query coordinator 3304 can finalize the results and communicatethem to the search head 210.

In some cases, the query coordinator 3304 can use network topologyinformation of the machines that will be executing the query to generatethe instructions for the nodes. For example, the query coordinator 3304can use the physical location of the processors that will execute thequery to generate the instructions. As one example, the querycoordinator 3304 can indicate that it is preferred that the processorsassigned to execute the query be located on the same machine or close toeach other.

In some embodiments, the instructions for the nodes can be generated inthe form of a DAG, as described in greater detail above. The DAG caninclude the instructions for the nodes to carry out the processing tasksincluded in the DAG. In some cases, the DAG can include additionalinformation, such as instructions on how to select partitions for thedifferent tasks. For example, the DAG can indicate that it is preferablethat a partition that will be receiving data from another partition beon the same machine, or nearby machine, in order to reduce networktraffic.

In addition to generating instructions for the datasetsources/destinations and the nodes, the query coordinator 3304 cangenerate instructions for itself. In some cases, the instructionsgenerated for itself can depend on the query that is being processed,the capabilities of the nodes 3306, and the results expected from thenodes. For example, in some cases, the type of query requested mayrequire the query coordinator 3304 to perform more or less processing.For example, a cursored search may require more processing by the querycoordinator 3304 than a batch search. Accordingly, the query coordinator3304 can generate tasks or instructions for itself based on the queryrequested.

In addition, if the nodes 3306 are unable to perform certain tasks onthe data, then the query coordinator 3304 can assign those tasks toitself and generate instructions for itself based on those tasks.Similarly, based on the form of the data that the query coordinator 3304is expected to receive, it can generate instructions for itself in orderto finalize the results for reporting.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 3900. In some cases, one or more blocks can beomitted. Furthermore, it will be understood that the various blocksdescribed herein with reference to FIG. 39 can be implemented in avariety of orders. In some cases, the query coordinator 3304 canimplement some blocks concurrently or change the order as desired. Forexample, the query coordinator 3304 can obtain information about thedataset sources/destinations (3904), determine processing requirements(3906), and determine available resources (3908) concurrently or in anyorder, as desired. 15.0. WORKLOAD MONITORING AND ADVISING FLOW

FIG. 40 is a flow diagram illustrative of an embodiment of a routine4000 implemented by the system 3301 to generate a query processingscheme. One skilled in the relevant art will appreciate that theelements outlined for routine 4000 can be implemented by one or morecomputing devices/components that are associated with the system 3301,such as the search head 210, search process master 3301, querycoordinator 3304, indexer 206, and/or worker nodes 3306. Thus, thefollowing illustrative embodiment should not be construed as limiting.

At block 4002, the system 3301 tracks query-resource usage data. Asdescribed in greater detail above, the system 3301 can track detailedinformation related to queries that are executed by the system 3301,which in some embodiments can be stored in the workload catalog 3312, orotherwise stored to be accessible to the system 3301. For example, thesystem can track data indicating the resources used to execute thequeries or timing information indicating the amount of time a query tookto execute. Furthermore, the system can track information on a pertransformation level, indicating the resources used to perform aparticular task or transformation on a set of data, the amount of datainvolved, the time it took to perform the transformation, etc. In someembodiments, this information and other information related to previousqueries, datasets, and system components can be stored in the workloadcatalog 3312.

At block 4004, the system 3301 tracks resource utilization data. Asdescribed in greater detail above, the system 3301 can track detailedinformation related to utilization rates of system resources, which insome cases can be stored in the node monitoring module 3314. In someembodiments, the nodes 3306 can include monitoring modules 3410, whichcan monitor the utilization rates of processors, I/O, memory, and othercomponents of the nodes 3306. The information from the nodes 3306 of thesystem 3301 can be communicated to the node monitoring module 3314 forstorage. In some cases, each node 3306 can include at least onemonitoring module 3410. In certain embodiments, each node 3306 caninclude at least one monitoring module for each processor 3406 of thenode 3306.

At block 4006, the system 3301 receives a query, as described in greaterdetail above. At block 4008, the system 3301 defines a query processingscheme, as described in greater detail above. In some cases the system3301 can use the query-resource usage data and/or the resourceutilization data to define the query processing scheme.

In some embodiments, the system 3301 can use the query-resource usagedata to determine the amount of time the query will take to completecompared to the amount of resources assigned to process the query. Thesystem can use this information to determine an amount of resources toallocate based on query. For example, the system can compare thedatasets used for the received query with datasets used for previousqueries, the types of transformations required by the received querycompared to previous queries. Based on the comparison, the system 3301can determine the effect of the amount of resources assigned to thequery compared to the time to execute the query.

In certain embodiments, the system 3301 can further use the resourceutilization data to define the query processing scheme. For example, thesystem 3301 can determine the amount of resources that are currentlyavailable for use to execute the query. Based on the amount of currentlyavailable resources, the system 3301 can determine how many resourcesshould be allocated to the query. As an example, assume that based onthe query-resource usage data, the system 3301 determines that thirtyprocessors are preferred to process a query and that fewer than twentyprocessors would result in an undue delay. Based on the system 3301determining that thirty processors are available, the system 3301 canallocate all thirty processors or at least twenty for the query.

In some cases, the system 3301 can track usage over time to predictsurges in queries or determine whether additional queries are expectedin the near term. For example, the system 3301 may determine that thereis a surge in queries around 9:00 AM when most users begin work. Withcontinued reference to the example above, if the query is received at8:55 AM and the thirty processors are available, the system 3301 maydetermine to allocate twenty processors rather than the preferred thirtybecause a large number of queries are expected at 9:00 AM.

At block 4010, the system executes the query. In some cases, asdescribed in greater detail above, to execute the query, the systemcommunicates a query processing scheme to the nodes 3306. In turn thenodes obtain relevant data from the datasets, process the data, andreturn results to the query coordinator. The query coordinator performsany additional processing based on the query processing scheme andcommunicates the results to the search head 210 for display on theclient device 404.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 4000. For example, in some embodiments, theroutine 4000 can further include, monitoring nodes during queryexecution, allocating/deallocating resources based on the query,Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 40 can be implemented in a variety oforders. In some cases, the system 3301 can implement some blocksconcurrently or change the order as desired. For example, the system3301 can track query-resource usage data (4002), track resourceutilization of nodes (4004), and receive a query (4006) concurrently orin any order, as desired. Similarly, the system 3301 can track resourceutilization of nodes (4004) while executing the query (4010), etc.

16.0. Multiple Dataset Sources Flow

FIG. 41 is a flow diagram illustrative of an embodiment of a routine4100 implemented by the query coordinator 3304 to execute a query ondata from multiple dataset sources. Although described as beingimplemented by the query coordinator 3304, one skilled in the relevantart will appreciate that the elements outlined for routine 4100 can beimplemented by one or more computing devices/components that areassociated with the system 3301, such as the search head 210, searchprocess master 3301, indexer 206, and/or worker nodes 3306. Thus, thefollowing illustrative embodiment should not be construed as limiting.

At block 4102, the query coordinator 3304 receives a query, as describedin greater detail above with reference to block 3802 of FIG. 38 . Atblock 4104, the query coordinator identifies the dataset sources,including the indexers 206 as one dataset source, as described ingreater detail above with reference to block 3902 of FIG. 39 . The querycoordinator 3304 can also identify a second dataset source, such as anexternal data source, a common storage, an ingested data buffer, queryacceleration data store, etc.

At block 4106, the query coordinator 3304 generates a subquery for theindexers. As described herein, the subquery can be generated based onthe processing capabilities of the indexers. The subquery can indicateto the indexers that data to be processed by the indexers and the mannerof processing the data by the indexers. Further, the subquery caninstruct the indexers to provide the results (or partial results) of thesubquery to the nodes 3306 for further processing. Accordingly, usingthe subquery, the indexers can identify the data to process, process thedata, and communicate the results to the nodes 3306. The subquery can bein any query language, as described herein.

At block 4108, the query coordinator 3304 allocates partitions for asecond dataset. The partition allocation can be based on the informationabout the dataset and/or the query requirements, as described in greaterdetail in blocks 3906, 3908, and 3910 of FIG. 39 . At block 4110, thequery coordinator 3304 allocates partitions to combine the results (orpartial results) from the two datasets. Similar to block 4108, the querycoordinator 3304 can allocate partitions to combine the partial resultsfrom the different datasets based on the query requirements. Forexample, the query can include a command indicating that the resultsfrom different dataset sources are to be combined in some way.

At block 4112, the query coordinator 3304 executes the query asdescribed in greater detail above with reference to block 4010 of FIG.40 . In executing the query, the query coordinator 3304 can communicatethe subquery to the indexers 206 or embed the subquery into theinstructions to the nodes 3306 such that the nodes 3306 communicate thesubquery to the indexers 206.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 4100. For example, in some embodiments, theroutine 4100 can further include, monitoring nodes during queryexecution, allocating/deallocating resources based on the query, etc. Asanother example, in certain embodiments, the identification of thedataset sources, generation of a subquery and allocation of partitionscan form part of a processing query block, similar to the process queryblock 3804 of FIG. 38 . In some cases, the routine 4100 can includeallocating partitions to receive and process the partial results fromthe indexers prior to combining the partial results from the differentdatasets. In certain embodiments, the system 3301 can dynamicallyallocate partitions based on the number of indexers from which the nodes3306 will receive data. Furthermore, although described as interactingwith indexers 206, it will be understood that the system 3301 canprocess and execute query on any two or more dataset sources, and thatthe system 3301 can generate subqueries or instructions for the datasetsources or allocate partitions for the dataset sources based oninformation about the dataset sources. as described in greater detailherein.

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 41 can be implemented in a variety oforders. In some cases, the system 3301 can implement some blocksconcurrently or change the order as desired. For example, the system3301 can generate a subquery for the indexers (4106), allocatepartitions for the second dataset (4108), and allocate partitions tocombine partial results from the indexers and second dataset (4110)concurrently, or in any order, as desired.

17.0. External Data Source Flow

FIG. 42 is a flow diagram illustrative of an embodiment of a routine4200 implemented by the query coordinator 3304 to execute a query ondata from an external data source. Although described as beingimplemented by the query coordinator 3304, one skilled in the relevantart will appreciate that the elements outlined for routine 4200 can beimplemented by one or more computing devices/components that areassociated with the system 3301, such as the search head 210, searchprocess master 3301, indexer 206, and/or worker nodes 3306. Thus, thefollowing illustrative embodiment should not be construed as limiting.

At block 4202, the query coordinator 3304 receives a query, as describedin greater detail above with reference to block 3802 of FIG. 38 . Atblock 4204, the query coordinator identifies the external data sources,as described in greater detail above with reference to block 3902 ofFIG. 39 .

At block 4206, the query coordinator 3304 dynamically generates asubquery for the external data source. As described herein, the querycoordinator 3304 can generate the subquery for the external data sourcebased on information obtained about the external data source asdescribed herein with reference to, inter alia, blocks 3904 and 3910 ofFIG. 39 . The information can indicate the type of external data source,APIs and languages to use to interface with the external data source,the type and amount of data stored in the external data source. Inaddition, the information can indicate whether the external data sourcemultiple partitions, and if so, how many. Further, the information canindicate the location of the processors of the external data source withwhich the nodes 3306 will interact. The information can also indicatethe processing capabilities of the external data source, such as whatcommands or transformations the external data source can perform on thedata stored therein.

Using the information about the external data source, the querycoordinator 3304 can generate a subquery. In certain embodiments, thequery coordinator 3304 generates a subquery that tasks the external datasource with merely returning the data, performing some processing of thedata, or processing the data as much as it can based on itscapabilities. By pushing some processing of the data to the externaldata source, the query coordinator 3304 can reduce the processing loadon the system 3301.

At block 4208, the query coordinator 3304 allocates partitions toreceive and process results from the external data source. As describedherein, the query coordinator 3304 can allocate partitions based on thequery requirements and the data received from the external data source.For example, if the external data source can perform some processing onthe data, then the query coordinator 3304 can allocate partitions toreceive the results of the processing. If the subquery indicated thatthe external data source was to return results without processing them,then the query coordinator 3304 can allocate partitions to receive theunprocessed results from the external data source, and process themaccording to the query.

In addition, the query coordinator 3304 can allocate partitions based onthe number of partitions supported by the external data source. Forexample, if the external data source supports four partitions forreading data, then the query coordinator 3304 can allocate fourpartitions to read from each of the partitions supported by the externaldata source. However, it will be understood that the query coordinator3304 can allocate fewer or more partitions as desired. Further, thenumber of partitions allocated can be based on the resources availableon the system 3301.

In some cases, the query coordinator 3304 can allocate more partitionsthan is supported by the external data source and/or submit multiplesubqueries to the external data source. For example, if the externaldata source only supports a single partition, the query coordinator 3304can allocate multiple partitions to send different subqueries to theexternal data source and receive the results back. In this way, thequery coordinator 3304 can increase the number of parallel reads fromthe external data source. As a non-limiting example, suppose an externaldata source only supports one partition and the query indicates that adata based on an age range of 20-49 is to be obtained from the externaldata source. The query coordinator can break up the age range into foursets (20-29, 30-39, 40-49) and send (or have nodes send) a subquery foreach set to the external data source. The external data source canprocess the requests concurrently and return results, and may not knowthat the requests are coming from the same system 3301. In this way, thesystem 3301 can receive results in parallel from an external data sourcethat supports a single partition. The query coordinator 3304 cansimilarly send multiple subqueries to one partition of amulti-partition-supporting external data source to increase the parallelreads from the external data source.

At block 4210, the query coordinator 3304 executes the query asdescribed in greater detail above with reference to block 4110 of FIG.41 . It will be understood that fewer, more, or different blocks can beused as part of the routine 4200. For example, in some embodiments, theroutine 4200 can further include, monitoring nodes during queryexecution, allocating/deallocating resources based on the query, etc. Asanother example, in certain embodiments, the identification of theexternal data source, generation of a subquery and allocation ofpartitions can form part of a processing query block, similar to theprocess query block 3804 of FIG. 38 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 42 can be implemented in a variety oforders. In some cases, the system 3301 can implement some blocksconcurrently or change the order as desired. For example, the system3301 can generate a subquery for the external data source (4206) andallocate partitions concurrently (4208) or in any order, as desired.

18.0. Dataset Destination Flow

FIG. 43 is a flow diagram illustrative of an embodiment of a routine4300 implemented by the query coordinator 3304 to execute a query basedon a dataset destination. Although described as being implemented by thequery coordinator 3304, one skilled in the relevant art will appreciatethat the elements outlined for routine 4300 can be implemented by one ormore computing devices/components that are associated with the system3301, such as the search head 210, search process master 3301, indexer206, and/or worker nodes 3306. Thus, the following illustrativeembodiment should not be construed as limiting.

At block 4302, the query coordinator 3304 receives a query, as describedin greater detail above with reference to block 3802 of FIG. 38 . Atblock 4304, the query coordinator identifies the dataset destination, asdescribed in greater detail above with reference to block 3902 of FIG.39 . In some embodiments, the dataset destination can refer to thelocation where query results or partial query results are to be storedby the system 3301. For example, the nodes 3306 can process data fromany dataset source and then store the data in a dataset destination, aswell as provide the results to a client device 404. In some cases, thedataset destination can be the same as the dataset source. For example,data can be read from the ingested data buffer, processed, and thenstored back in the ingested data buffer. However, in certain cases, thedataset destination and dataset source are different. For example, insome embodiments, data is read from the common storage, processed by thenodes, and the results stored in the query acceleration data store 3308,an external data source 3318, an ingested data buffer, etc.

At block 4306, the query coordinator 3304 determines the functionalityof the dataset destination. As described herein with reference to interalia block 3904 of FIG. 39 , each dataset destination, like datasetsources, can have different functionality and capabilities. Thisfunctionality can correspond to how to communicate with the datasetdestination (e.g., the number of partitions supported by the datasetdestination, the APIs, language, or communication protocols of thedataset destination), processing supported by the dataset destination(e.g., commands supported by the dataset destination), etc.

At block 4308, the query coordinator 3304 allocates partitions toprocess and communicate results to the dataset destination. Similar toallocating partitions to receive data from a dataset source, the querycoordinator 3304 can allocate partitions to process and communicate datato a dataset destination. For example, the query coordinator 3304 canallocate partitions based on the partitions supported by the datasetdestination, the processing capabilities of the dataset destination,etc. As part of allocating partitions, the query coordinator 3304 caninstruct the partitions on how to communicate the data to the datasetdestination, include translated commands for the dataset destination,etc.

At block 4310, the query coordinator 3304 executes the query asdescribed in greater detail above with reference to block 4110 of FIG.41 . It will be understood that fewer, more, or different blocks can beused as part of the routine 4300. For example, in some embodiments, theroutine 4300 can further include, monitoring nodes during queryexecution, allocating/deallocating resources based on the query, etc. Asanother example, in certain embodiments, the identification of thedataset destination, determination of dataset destination functionality,allocation of partitions can form part of a processing query block,similar to the process query block 3804 of FIG. 38 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 43 can be implemented in a variety oforders. In some cases, the system 3301 can implement some blocksconcurrently or change the order as desired. For example, the system3301 can determine dataset destination functionality (4306) and allocatepartitions (4308) concurrently or in any order, as desired.

19.0. Serialization and Deserialization Flow

FIG. 44 is a flow diagram illustrative of an embodiment of a routine4400 implemented by a serialization module, of a component of the dataintake and query system 3301 to serialize data for communication to adestination, similar to the serialization/deserialization module 3412 ofFIG. 34 . The destination can be another component of the data intakeand query system 3301 or external to the data intake and query system3301. Although described as being implemented by serialization module,one skilled in the relevant art will appreciate that the elementsoutlined for routine 4300 can be implemented by one or more computingdevices/components that are associated with the system 3301, such as thesearch head 210, search process master 3301, indexer 206, and/or workernodes 3306. Thus, the following illustrative embodiment should not beconstrued as limiting.

At block 4402, the serialization module identifies events forserialization. In some cases, as part of identifying the events forserialization, the serialization module groups the events. In someembodiments, the serialization module identifies the events forserialization based on a common source or sourcetype of the events, orother shared attribute, or based on a destination for the events. Incertain embodiments, the serialization module identifies events forserialization based on timing information. For example, theserialization module can serialize events received within a certain timeperiod, such as one second, ten second, one minute, etc.

At block 4404, the serialization module determines header informationfor the events. The header information can include the number of eventsin a group, the field names for the events in the group, etc. In somecases, the field names in the header can include all field names acrossall events. For example, if some events have different field names, bothcan be included in the header information. In some cases, the headerinformation can also include mapping information for mapping field namesto field positions (e.g., where a particular field name is locatedwithin an event, etc.). In some embodiments, as part of determining theheader information for the events, the serialization module canserialize the header information. For example, if some field names arerepetitive or have been identified before in previous groups, they canbe replaced with an identifier indicating a cache entry that has thatfield name. The identifier can be used by the receiving component todeserialize the data. Furthermore, the serialization module can updatethe cache based on the header information. For example, if some of theheader information had not been seen before, the serialization modulecan update the cache so that an identifier can be used in place of theheader information in the future.

At block 4406, the serialization module serializes the events. As partof serializing the events, the serialization module can identify fieldvalues in the events and determine whether the field values in eachevent are stored in cache. The field values that are stored in cache canbe replaced with cache identifiers. In addition, the serializationmodule can identify data other data for removal. For example, in someembodiments, certain delimiters, such as ‘,’ or ‘\n’ can be removed fromthe events.

Further, as part of serializing the events, the serialization module canupdate the cache or generate update cache commands for the receiver.Updating the cache can include adding entries for data encountered inthe events or removing entries that have not been used recently. Thecache can be updated with each event or each group and can be performedprior to, after, or concurrently with an event. For example, uponreceiving a group of events, the receiver can update the cache and thenprocess the events, update the cache while processing the events, orupdate the cache after the events are processed. In some cases, thereceiver updates the cache following each event. In some cases, newentries are added to the cache prior to processing the events andentries are removed from the cache after processing the events in agroup.

At 4408, the serialization module communicates the serialized events tothe destination. In some cases, the serialization module communicatesthe events in a streaming fashion. In such embodiments, theserialization module communicates the events once the serializationprocess for that event is completed. In certain embodiments, theserialization module communicates the events as a group. In suchembodiments, the serialization module waits until the group of events isserialized before transmitting the events as a group.

As part of generating the group and serializing the data, theserialization/deserialization module 3412 can determine the number ofevents to group, determine the order and field names for the fields inthe events of the group, parse the events, determine the number offields for each event, identify and serialize serializable field valuesin the event fields, and identify cache deltas. In some cases, theserialization/deserialization module 3412 performs the various tasks ina single pass of the data, meaning that it performs the identification,parsing, and serializing during a single review of the data. In thismanner, the serialization/deserialization module 3412 can operate onstreaming data and avoid adding delay to theserialization/deserialization process.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 4400. For example, in some embodiments, theroutine 4400 can further include, building and updating the cache at thereceiver, etc. Furthermore, it will be understood that the variousblocks described herein with reference to FIG. 44 can be implemented ina variety of orders. In some cases, the serialization module canimplement some blocks concurrently or change the order as desired. Forexample, the serialization module can determine header information(4404) and serialize the events (4406) concurrently or in any order, asdesired. Furthermore, although not explicitly described herein, it willbe understood that the data can be deserialized in a similar manner.That is, the receiver can determine the number of events in the groupand the fields based on the header information and deserialize eachevent using the cache and data in the serialized group.

20.0. Accelerated Query Results Flow

FIG. 45 is a flow diagram illustrative of an embodiment of a routine4500 implemented by the query coordinator 3304 to execute a queryutilizing a data store (e.g., query acceleration data store 3308).Although described as being implemented by the query coordinator 3304,one skilled in the relevant art will appreciate that the elementsoutlined for routine 4500 can be implemented by one or more computingdevices/components that are associated with the system 3301, such as thesearch head 210, search process master 3301, indexer 206, and/or workernodes 3306. Thus, the following illustrative embodiment should not beconstrued as limiting.

At block 4502, the query coordinator 3304 receives a query, as describedin greater detail above with reference to block 3802 of FIG. 38 . In theexample of FIG. 45 , the query can reference a particular dataset storedby the query acceleration data store 3308, and reference informationwhich is to be obtained from another dataset source (e.g., external datasource 3318, ingested data buffer, common storage, indexers 206, etc.).

At block 4504, first partial results are identified. As described above,a query can indicate datasets, including a particular dataset that isstored in the query acceleration data store 3308. The query accelerationdata store 3308 can store datasets that are indicated (e.g., by users,for example based on the users including a particular command) asbenefiting from storage in the query acceleration data store 3308 (e.g.,benefitting from caching). In addition, the datasets stored in the queryacceleration data store 3308 can correspond to results or partialresults of queries previously processed by the system 3301. The querycoordinator 3304 can determine that the received query references one ormore datasets stored by the query acceleration data store. For example,the query may specify a dataset is stored in the query acceleration datastore 3308 and/or provide a unique identifier associated with a storeddataset, and the system 3301 (e.g., the query coordinator 3304) mayrelay this unique identifier to the worker nodes 3306 to obtain thereferenced dataset(s). In certain cases, the system 3301 can prompt theuser with identifiers of datasets stored in the query acceleration datastore 3308.

In some cases, the query coordinator 3304 can intelligently determinethat a portion of the data identified for processing in the querycorresponds to data that was previously processed. For example, thequery coordinator 3304 can compare the query with previous queries. Thecomparison can be made against all queries received by the system orqueries received by the system from a particular user or group of users.As yet another example, suppose a query indicates that the last sixtyminutes of data from a particular dataset source is to be processed. Thequery coordinator 3304 can compare the query with previous queries fromthe user and determine that a similar query was received thirty minutespreviously indicating that the prior thirty minutes of data from thedataset source was to be processed and the results of the query storedin the query acceleration data store 3308. Based on that information,the query coordinator 3304 can determine that the first thirty minutesof the sixty minutes' worth of data has already been processed and theresults are accessible in the query acceleration data store 3308.

As described above, worker nodes 3306 can utilize the particular datasetobtained from the data store to determine results. Since the queryacceleration data store 3308 stores the particular dataset, firstpartial results can be rapidly identified by the worker nodes 3306, andthe query coordinator 3304 can provide the first partial results to arequesting client. For example, the first partial results may beminimally processed data that was previously obtained from anotherdataset source (e.g., an external data source 3318, indexers 3306,ingested data buffer) and stored in the query acceleration data store3308 with little or no processing by the worker nodes 3306. For example,the worker nodes 3306 may have imported the data from an external datasource 3318 and stored the received data as-is in the query accelerationdata store 3308. The imported results can correspond to raw machine dataor processed data.

Additionally, the first partial results can correspond to results orpartial results of a previous query that were obtained after datareceived by a dataset source was processed the worker nodes 3306. Forexample, the worker nodes 3306 may have imported the data from anexternal data source 3318, ingested data buffer, indexers 3306, or evendata stored in the query acceleration data store 3308, performed one ormore transformations on the data, (e.g., extracted relevant portions,combined the data with results from other dataset sources, etc.), andthen stored the results of the processing in the query acceleration datastore 3308.

At block 4506, the query coordinator 3304 dynamically allocatespartitions. The partitions can be allocated to receive and process datafrom a dataset source referenced in the received query (second portionof the set of data), combine results of processing the data from thedataset source (second partial results) with the first partial results,process the combined results, and communicate the results to adestination, such as the query coordinator 3304, search head 210, clientdevice 404, or a dataset destination. As described in block 4504, thequery can indicate a particular dataset stored in the query accelerationdata store 3308. Additionally, the query can further indicate that datais to be obtained from another dataset source, processed, and the secondpartial results combined with the first partial results. The querycoordinator 3304 can allocate partitions based on the query requirementsand the data received from the dataset source as described herein. Insome cases, the query does not indicate that the first partial resultsare stored in the query acceleration data store 3308. In suchembodiments, the query can identify a dataset source for obtaining dataand the query coordinator 3304 can analyze the query to determine that afirst portion of the data requested corresponds to the first partialresults stored in the query acceleration data store 3308.

In some embodiments, the dynamic allocation of partitions can includeallocating partitions to receive and process the first partial resultsfrom the query acceleration data store 3308. In addition, in some cases,the query coordinator 3304 can allocate one or more partitions to storethe second partial results or combined results in the accelerate datastore 3308 for later use, similar to the first partial results.

At block 4508, the query coordinator 3304 executes the query asdescribed in greater detail above with reference to block 4110 of FIG.41 . It will be understood that fewer, more, or different blocks can beused as part of the routine 4500. For example, in some embodiments, theroutine 4500 can further include, monitoring nodes during queryexecution, allocating/deallocating resources based on the query, etc. Asanother example, in certain embodiments, identification of the firstpartial results and allocation of partitions can form part of aprocessing query block, similar to the process query block 3804 of FIG.38 . Further, the first partial results can be communicated to theclient as-is or further processed by the worker nodes 3306 (e.g., logiccan be applied to the first partial results), and then provided to therequesting client.

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 45 can be implemented in a variety oforders. In some cases, the system 3301 can implement some blocksconcurrently or change the order as desired. For example, the querycoordinator 3304 can identify the first partial results (4504) andallocate partitions (4506) concurrently or in any order, as desired.During execution, the nodes can concurrently obtain the first partialresults from the query acceleration data store 3308 and obtain andprocess other data from another dataset source, or concurrently providethe first partial results to the query coordinator 3304 or client device404 and obtain and process other data from another dataset source, etc.

21.0. Common Storage Architecture

As discussed above, indexers 206 may in some embodiments operate both toingest information into a data intake and query system 3301, and tosearch that information in response to queries from client devices 404.The use of an indexer 206 to both ingest and search information may bebeneficial, for example, because indexers 206 may have ready access toinformation that they have ingested, and thus be enabled to quicklyaccess that information for searching purposes. However, use of anindexer 206 to both ingest and search information may not be desirablein all instances. As an illustrative example, consider an instance inwhich information within the system 3301 is organized into buckets, andeach indexer 206 is responsible for maintaining buckets within a datastore 208 corresponding to the indexer 206. Illustratively, a set of 10indexers 206 may maintain 100 buckets, distributed evenly across tendata stores 208 (each of which is managed by a corresponding indexer206). Information may be distributed throughout the buckets according toa load-balancing mechanism used to distribute information to theindexers 206 during data ingestion. In an idealized scenario,information responsive to a query would be spread across the 100buckets, such that each indexer 206 may search their corresponding 10buckets in parallel, and provide search results to a search head 360.However, it is expected that this idealized scenario may not alwaysoccur, and that there will be at least some instances in whichinformation responsive to a query is unevenly distributed across datastores 208. As an extreme example, consider a query in which responsiveinformation exists within 10 buckets, all of which are included in asingle data store 208 associated with a single indexer 206. In such aninstance, a bottleneck may be created at the single indexer 206, and theeffects of parallelized searching across the indexers 206 may beminimal. To increase the speed of operation of search queries in suchcases, it may therefore be desirable to configure the data intake andquery system 3301 such that parallelized searching of buckets may occurindependently of the operation of indexers 206.

Another potential disadvantage in utilizing an indexer 206 to bothingest and search data is that computing resources of the indexers 206may be split among those two tasks. Thus, ingestion speed may decreaseas resources are used to search data, or vice versa. It may further bedesirable to separate ingestion and search functionality, such thatcomputing resources available to either task may be scaled ordistributed independently.

One example of a configuration of the data intake and query system 3301that enables parallelized searching of buckets independently of theoperation of indexers 206 is shown in FIG. 46 . The embodiment of system3301 that is shown in FIG. 46 substantially corresponds to embodiment ofthe system 3301 as shown in FIG. 33 , and thus corresponding elements ofthe system 3301 will not be re-described. However, unlike the embodimentas shown in FIG. 33 , where individual indexers 206 are assigned tomaintain individual data stores 208, the embodiment of FIG. 46 includesa common storage 4602. Common storage 4602 may correspond to any datastorage system accessible to each of the indexers 206. For example,common storage 4602 may correspond to a storage area network (SAN),network attached storage (NAS), other network-accessible storage system(e.g., a ho33sted storage system, which may also be referred to as“cloud” storage), or combination thereof. The common storage 4602 mayinclude, for example, hard disk drives (HDDs), solid state storagedevices (SSDs), or other substantially persistent or non-transitorymedia. Data stores 208 within common storage 4602 may correspond tophysical data storage devices (e.g., an individual HDD) or a logicalstorage device, such as a grouping of physical data storage devices or avirtualized storage device hosted by an underlying physical storagedevice. In one embodiment, common storage 4602 may be multi-tiered, witheach tier providing more rapid access to information stored in thattier. For example, a first tier of the common storage 4602 may bephysically co-located with indexers 206 and provide rapid access toinformation of the first tier, while a second tier may be located in adifferent physical location (e.g., in a hosted or “cloud” computingenvironment) and provide less rapid access to information of the secondtier. Distribution of data between tiers may be controlled by any numberof algorithms or mechanisms. In one embodiment, a first tier may includedata generated or including timestamps within a threshold period of time(e.g., the past seven days), while a second tier or subsequent tiersincludes data older than that time period. In another embodiment, afirst tier may include a threshold amount (e.g., n terabytes) orrecently accessed data, while a second tier stores the remaining lessrecently accessed data. In one embodiment, data within the data stores208 is grouped into buckets, each of which is commonly accessible to theindexers 206. The size of each bucket may be selected according to thecomputational resources of the common storage 4602 or the data intakeand query system 3301 overall. For example, the size of each bucket maybe selected to enable an individual bucket to be relatively quicklytransmitted via a network, without introducing excessive additional datastorage requirements due to metadata or other overhead associated withan individual bucket. In one embodiment, each bucket is 750 megabytes insize.

The indexers 206 may operate to communicate with common storage 4602 andto generate buckets during ingestion of data. Data ingestion may besimilar to operations described above. For example, information may beprovided to the indexers 206 by forwarders 204, after which theinformation is processed and stored into buckets. However, unlike someembodiments described above, the buckets may be stored in common storage4602, rather than in a data store 208 maintained by an individualindexer 206. Thus, the common storage 4602 can render information of thedata intake and query system 3301 commonly accessible to elements ofthat system 3301. As will be described below, such common storage 4602can beneficial enable parallelized searching of buckets to occurindependently of the operation of indexers 206.

As noted above, it may be beneficial in some instances to separatewithin the data intake and query system 3301 functionalities ofingesting data and searching for data. As such, in the illustrativeconfiguration of FIG. 46 , worker nodes 3306 may be enabled to searchfor data stored within common storage 4602. The nodes 3306 may thereforebe communicatively attached (e.g., via a communication network) with thecommon storage 4602, and be enabled to access buckets within the commonstorage 4602. The nodes 3306 may search for data within buckets in amanner similar to how searching may occur at the indexers 206, asdiscussed in more detail above. However, because nodes 3306 in someinstances are not statically assigned to individual data stores 208 (andthus to buckets within such a data store 208), the buckets searched byan individual node 3306 may be selected dynamically, to increase theparallelization with which the buckets can be searched. For example,using the example provided above, consider again an instance whereinformation is stored within 100 buckets, and a query is received at thedata intake and query system 3301 for information within 10 suchbuckets. Unlike the example above (in which only indexers 206 alreadyassociated with those 10 buckets could be used to conduct a search), the10 buckets holding relevant information may be dynamically distributedacross worker nodes 3306. Thus, if 10 worker nodes 3306 are available toprocess a query, each worker node 3306 may be assigned to retrieve andsearch within 1 bucket, greatly increasing parallelization when comparedto the low-parallelization scenario discussed above (e.g., where asingle indexer 206 is required to search all 10 buckets). Moreover,because searching occurs at the worker nodes 3306 rather than atindexers 206, computing resources can be allocated independently tosearching operations. For example, worker nodes 3306 may be executed bya separate processor or computing device than indexers 206, enablingcomputing resources available to worker nodes 3306 to scaleindependently of resources available to indexers 206.

Operation of the data intake and query system 3301 to utilize workernodes 3306 to search for information within common storage 4602 will nowbe described. As discussed above, a query can be received at the searchhead 360, processed at the search process master 3302, and passed to aquery coordinator 3304 for execution. The query coordinator 3304 maygenerate a DAG corresponding to the query, in order to determinesequences of search phases within the query. The query coordinator 3304may further determine based on the query whether each branch of the DAGrequires searching of data within the common storage 4602 (e.g., asopposed to data within external storage, such as remote systems 414 and416).

It will be assumed for the purposes of described that at least onebranch of the DAG requires searching of data within the common storage4602, and as such, description will be provided for execution of such abranch. While interactions are described for executing a single branchof a DAG, these interactions may be repeated (potentially concurrentlyor in parallel) for each branch of a DAG that requires searching of datawithin the common storage 4602. As discussed above with reference toFIG. 36 , executing a search representing a branch of a DAG can includea number of phases, such as an intake phase 3604, processing phase 3606,and collector phase 3608. It is therefore illustrative to discussexecution of a branch of a DAG that requires searching of the commonstorage 4602 with reference to such phases. As also discussed above,each phase may be carried out by a number of partitions, each of whichmay correspond to a worker node 3306 (e.g., a specific worker node 3306,processor within the worker node 3306, execution environment within aworker node 3306, such as a virtualized computing device orsoftware-based container, etc.).

When a branch requires searching within common storage 4602, the querycoordinator 3304 can select a partition (e.g., a processor within aworker node 3306) at random or according to a load-balancing algorithmto gather metadata regarding the information within the common storage4602, for use in dynamically assigning partitions (each implemented by aworker node 3306) to implement an intake phase 3604. Metadata isdiscussed in more detail above, but may include, for example, dataidentifying a host, a source, and a source type related to a bucket ofdata. Metadata may further indicate a range of timestamps of informationwithin a bucket. The metadata can then be compared against a query todetermine a subset of buckets within the common storage 4602 that maycontain information relevant to a query. For example, where a queryspecifies a desired time range, host, source, source type, orcombination thereof, only buckets in the common storage 4602 thatsatisfy those specified parameters may be considered relevant to thequery. In one embodiment, the subset of buckets is determined by theassigned partition, and returned to the query coordinator 3304. Inanother embodiment, the metadata retrieved by a partition is returned tothe query coordinator 3304 and used by the query coordinator 3304 todetermine the subset of buckets.

Thereafter, the query coordinator 3304 can dynamically assign partitionsto intake individual buckets within the determined subset of buckets. Inone embodiment, the query coordinator 3304 attempts to maximizeparallelization of the intake phase 3604, by attempting to intake thesubset of buckets with a number of partitions equal to the number ofbuckets in the subset (e.g., resulting in a one-to-one mapping ofbuckets in the subset to partitions). However, such parallelization maynot be feasible or desirable, for example, where the total number ofpartitions is less than the number of buckets within the determinedsubset, where some partitions are processing other queries, or wheresome partitions should be left in reserve to process other queries.Accordingly, the query coordinator 3304 may interact with the workloadadvisor 3310 to determine a number of partitions that are to be utilizedto conduct the intake phase 3604 of the query. Illustratively, the querycoordinator 3304 may initially request a one-to-one correspondencebetween buckets and partitions, and the workload advisor 3310 may reducethe number of partitions used for the intake phase 3604 of the query,resulting in a 2-to-1, 3-to-1, or n-to-1 correspondence between bucketsand partitions. Operation of the workload advisor 3310 is described inmore detail above.

The query coordinator 3304 can then assign the partitions (e.g., thosepartitions identified by interaction with the workload advisor 3310) tointake the buckets previously identified as potentially containingrelevant information (e.g., based on metadata of the buckets). In oneembodiment, the query coordinator 3304 may assign all buckets as asingle operation. For example, where 10 buckets are to be searched by 5partitions, the query coordinator 3304 may assign 2 buckets to a firstpartitions, two buckets to a second partitions, etc. In anotherembodiment, the query coordinator 3304 may buckets iteratively. Forexample, where 10 buckets are to be searched by 5 partitions, the querycoordinator 3304 may initially assign five buckets (e.g., one buckets toeach partition), and assign additional buckets to each partition as therespective partitions complete intake of previously assigned buckets.

In some instances, buckets may be assigned to partitions randomly, or ina simple sequence (e.g., a first partitions is assigned a first bucket,a second partitions is assigned a second bucket, etc.). In otherinstances, the query coordinator 3304 may assign buckets to partitionsbased on buckets previously assigned to a partitions, in a prior orcurrent search. Illustratively, in some embodiments each worker node3306 may be associated with a local cache of information (e.g., inmemory of the partitions, such as random access memory [“RAM”] ordisk-based cache). Each worker node 3306 may store copies of one or morebuckets from the common storage 4602 within the local cache, such thatthe buckets may be more rapidly searched by partitions implemented onthe worker node 3306. The query coordinator 3304 may maintain orretrieve from worker nodes 3306 information identifying, for eachrelevant node 3306, what buckets are copied within local cache of therespective nodes 3306. Where a partition assigned to execute a search isimplemented by a worker node 3306 that has within its local cache a copyof a bucket determined to be potentially relevant to the search, thatpartition may be preferentially assigned to search that locally-cachedbucket. In some instances, local cache information can further be usedto determine the partitions to be used to conduct a search. For example,partitions corresponding to worker nodes 3306 that have locally-cachedcopies of buckets potentially relevant to a search may be preferentiallyselected by the query coordinator 3304 or workload advisor 3310 toexecute the intake phase 3604 of a search. In some instances, the querycoordinator 3304 or other component of the system 3301 (e.g., the searchprocess master 3302) may instruct worker nodes 3306 to retrieve andlocally cache copies of various buckets from the common storage 4602,independently of processing queries. In one embodiment, the system 3301is configured such that each bucket from the common storage 4602 islocally cached on at least one worker node 3306. In another embodiment,the system 3301 is configured such that at least one bucket from thecommon storage 4602 is locally cached on at least two worker nodes 3306.Caching a bucket on at least two worker nodes 3306 may be beneficial,for example, in instances where different queries both require searchingthe bucket (e.g., because the at least two worker nodes 3006 may processtheir respective local copies in parallel). In still other embodiments,the system 3301 is configured such that all buckets from the commonstorage 4602 are locally cached on at least a given number n of workernodes 3306, wherein n is defined by a replication factor on the system3301. For example, a replication factor of 5 may be established toensure that 5 searches of buckets can be executed concurrently by 5different worker nodes 3306, each of which has locally cached a copy ofa given bucket potentially relevant to the searches.

In some embodiments, buckets may further be assigned to partitions toassist with time ordering of search results. For example, where a searchrequests time ordering of results, the query coordinator 3304 mayattempt to assign buckets with overlapping time ranges to the samepartition, such that information within the buckets can be sorted at thepartition. Where the buckets assigned to different partitions arenon-overlapping in time, the query coordinator 3304 may sort informationfrom different partitions according to an absolute ordering of thebuckets processed by the different partitions. That is, if alltimestamps in all buckets processed by a first worker node 3306 occurprior to all timestamps in all buckets processed by a second worker node3306, query coordinator 3304 can quickly determine (e.g., withoutreferencing timestamps of information) that all information identifiedby the first worker node 3306 in response to a search occurs in timeprior to information identified by the second worker node 3306 inresponse to the search. Thus, assigning buckets with overlapping timeranges to the same partition can reduce computing resources needed totime-order results.

In still more embodiments, partitions may be assigned based on overlapsof computing resources of the partitions. For example, where a partitionis required to retrieve a bucket from common storage 4602 (e.g., where alocal cached copy of the bucket does not exist on the worker node 3306implementing the partition), such retrieval may use a relatively highamount of network bandwidth or disk read/write bandwidth on the workernode 3306 implementing the partition. Thus, assigning a second partitionof the same worker node 3306 might be expected to strain or exceed thenetwork or disk read/write bandwidth of the worker node 3306. For thisreason, it may be preferential to assign buckets to partitions such thattwo partitions within a common worker node 3306 are not both required toretrieve buckets from the common storage 4602. Illustratively, it may bepreferential to evenly assign all buckets containing potentiallyrelevant information among the different worker nodes 3306 used toimplement the intake phase 3604. For similar reasons, where a givenworker node 3306 has within its local cache two buckets that potentiallyinclude relevant information, it may be preferential to assign both suchbuckets to different partitions implemented by the same worker node3306, such that both buckets can be search in parallel on the workernode 3306 by the respective partitions. In some instances, commonalityof computing resources between partitions can further be used todetermine the partitions to be used to conduct an intake phase 3604. Forexample, the query coordinator 3304 may preferentially select partitionsthat are implemented by different worker nodes 3306 (e.g., in order tomaximize network or disk read/write bandwidth) to implement an intakephase 3604. However, where a worker node 3306 has locally cachedmultiple buckets with information potentially relevant to the search,the query coordinator 3304 may preferentially multiple partitions onthat worker node 3306 (e.g., up to a number of partitions equal to thenumber of potentially-relevant buckets stored at the worker node 3306).

The above mechanisms for assigning buckets to partitions may be combinedbased on priorities of each potential outcome. For example, the querycoordinator 3304 may give an initial priority to distributing assignedpartitions across a maximum number of different worker nodes 3306, but ahigher priority to assigning partitions to process buckets withoverlapping timestamps. The query coordinator 3304 may give yet a higherpriority to assigning partitions to process buckets that have beenlocally cached. The query coordinator 3304 may still further give higherpriority to ensuring that each partition is searching at least onebucket for information responsive to a query at any given time. Thus,the query coordinator 3304 can dynamically alter the assignment ofbuckets to partitions to increase the parallelization of a search, andto increase the speed and efficiency with which the search is executed.

When searching for information within the common storage 4602, theintake phase 3604 may be carried out according to bucket-to-partitionmapping discussed above, as determined by the query coordinator 3304.Specifically, after assigning at least one bucket to each partition tobe used during the intake phase 3604, each partition may begin toretrieve its assigned bucket. Retrieval may include, for example,downloading the bucket from the common storage 4602, or locating a copyof the bucket in a local cache of a worker node 3306 implementing thepartition. Thereafter, each partition may conduct an initial search ofthe bucket for information responsive to a query. The initial search mayinclude processing that is expected to be disk or network intensive,rather than processing (e.g., CPU) intensive. For example, the initialsearch may include accessing the bucket, which may include decompressingthe bucket from a compressed format, and accessing an index file storedwithin the bucket. The initial search may further include referencingthe index or other information (e.g., metadata within the bucket) tolocate one or more portions (e.g., records or individual files) of thebucket that potentially contain information relevant to the search.

Thereafter, the search proceeds to the processing phase 3606, where theportions of buckets identified during the intake phase 3604 are searchedto locate information responsive to the search. Illustratively, thesearching that occurs during the processing phase 3606 may be predictedto be more processor (e.g., CPU) intensive than that which occurredduring the intake phase 3604. As such, the number of partitions used toconduct the processing phase 3606 may vary from that of the intake phase3604. For example, during or after the conclusion of the intake phase3604, each partition implementing that phase 3604 may communicate to thequery coordinator 3304 information regarding the portions identified aspotentially containing information relevant to the query (e.g., thenumber, size, or formatting of portions, etc.). The query coordinator3304 may thereafter determine from that information (e.g., based oninteractions with the workload advisor 3310) the partitions to be usedto conduct the processing phase 3606. In other embodiments, the querycoordinator 3304 may select partitions to be used to conduct theprocessing phase 3606 prior to implementation of the intake phase 3604(e.g., contemporaneously with selecting partitions to conduct the intakephase 3604). The partitions selected for conducting the processing phase3606 may include one or more partitions that previously conducted theintake phase 3604. However, because the processing phase 3606 may beexpected to be more resource intensive than the intake phase 3604 (e.g.,with respect to use of processing cycles), the number of partitionsselected for conducting the processing phase 3606 may exceed the numberof partitions that previously conducted the intake phase 3604. Tominimize network communications, the additional partitions selected toconduct the processing phase 3606 may be preferentially selected to becollocated on a worker node 3306 with a partition that previouslyconducted the intake phase 3604, such that portions of buckets to beprocessed by the additional partitions can be received from a partitionon that worker node 3306, rather than being transmitted across anetwork.

At the processing phase 3606, the partitions may parse the portions ofbuckets located during the intake phase 3604 in order to identifyinformation relative to a search. For example, the may parse theportions of buckets (e.g., individual files or records) to identifyspecific lines or segments that contain values specified within thesearch, such as one or more error types desired to be located during thesearch. Where the search is conducted according to map-reducetechniques, the processing phase 3606 can correspond to implementing amap function. Where the search requires that results be time-ordered,the processing phase 3606 may further include sorting results at eachpartition into a time-ordering.

The remainder of the search may be executed in phases according to theDAG determined by the query coordinator 3304. For example, where thebranch of the DAG currently being processed includes a collection node,the search may proceed to a collector phase 3608. The collector phase3608 may be executed by one or more partitions selected by the querycoordinator 3304 (e.g., based on the information identified during theprocessing phase 3606), and operate to aggregate information identifiedduring the processing phase 3606 (e.g., according to a reduce function).Where the processing phase 3606 represents a top-node of a branch of theDAG being executed, the information located by each partition during theprocessing phase 3606 may be transmitted to the query coordinator 3304,where any additional nodes of the DAG are completed, and search resultsare transmitted to a data destination 3616. These additional phases maybe implemented in a similar manner as described above, and they aretherefore not discussed in detail with respect to searches against acommon storage 4602.

As will be appreciated in view of the above description, the use of acommon storage 4602 can provide many advantages within the data intakeand query system 3301. Specifically, use of a common storage 4602 canenable the system 3301 to decouple functionality of data ingestion, asimplemented by indexers 206, with functionality of searching, asimplemented by partitions of worker nodes 3306. Moreover, becausebuckets containing data are accessible by each worker node 3306, a querycoordinator 3304 can dynamically allocate partitions to buckets at thetime of a search in order to maximize parallelization. Thus, use of acommon storage 4602 can substantially improve the speed and efficiencyof operation of the system 3301.

22.0. Common Storage Flow

FIG. 47 is a flow diagram illustrative of an embodiment of a routine4700 implemented by the query coordinator 3304 to execute a query ondata within common storage 4602. Although described as being implementedby the query coordinator 3304, one skilled in the relevant art willappreciate that the elements outlined for routine 4700 can beimplemented by one or more computing devices/components that areassociated with the system 3301, such as the search head 360, searchprocess master 3301, indexer 206, and/or worker nodes 3306. Thus, thefollowing illustrative embodiment should not be construed as limiting.

At block 4702, the query coordinator 3304 receives a query, as describedin greater detail above with reference to block 3802 of FIG. 38 . Atblock 4704, the query coordinator identifies the common storage 4602 asa data source for the query (e.g., based on parameters of the query,based on timing requirements as described in greater detail above withreference to block 3902 of FIG. 39 , etc.).

At block 4706, the query coordinator 3304 determines one or more bucketswithin the common storage 4602 that may contain potentially relevantinformation for the query. As noted above, the one or more buckets maybe identified based on metadata of the buckets within common storage4602, including time ranges, sources, source types, or hosts related toinformation stored within each bucket. In one embodiment, the querycoordinator 3304 may utilize a partition of a worker node 3306 toretrieve current metadata of buckets within the common storage 4602, andthe query coordinator 3304 may utilize this information to determinepotentially relevant buckets. In another embodiment, the querycoordinator 3304 may direct a partition to retrieve current metadata ofbuckets within the common storage 4602 and to utilize this informationto determine potentially relevant buckets, after which the partition maynotify the query coordinator 3304 of the potentially relevant buckets.

At block 4708, the query coordinator 3304 allocates partitions to intakethe potentially relevant buckets during an intake phase 3604. Asdescribed above, the query coordinator 3304 can allocate partitionsbased on a number of factors, including a number of potentially relevantbuckets, a number of partitions available to intake the buckets, anumber of potentially relevant buckets that exist as cached copieswithin local storage of a worker node 3306, or a distribution ofpartitions across different worker nodes 3306 (e.g., to maximize anavailability of network or disk read/write bandwidth). In someembodiments, the query coordinator 3304 may interact with the workloadadvisor 3310 to allocate partitions to intake potentially relevantbuckets. In general, partitions may be allocated to intake potentiallyrelevant buckets in a manner that maximizes either or both of use oflocally-cached copies of buckets on worker nodes 3306 andparallelization of retrieval of buckets from common storage 4602.

At block 4710, the query coordinator 3304 executes the query asdescribed in greater detail above with reference to FIGS. 36 and 46 . Itwill be understood that fewer, more, or different blocks can be used aspart of the routine 4700. For example, in some embodiments, the routine4700 can further include allocating partitions to conduct subsequentphases of a query, such as a processing phase 3606 or collection phase3608. As another example, in certain embodiments, the identification ofthe common storage 4602, determination of potentially relevant buckets,and allocation of partitions to perform an intake phase 3604 can formpart of a processing query block, similar to the process query block3804 of FIG. 38 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 47 can be implemented in a variety oforders. In some cases, the system 3301 can implement some blocksconcurrently or change the order as desired. For example, the system3301 can in some instances allocate partitions to intake potentiallyrelevant buckets iteratively, during execution of a query (e.g., byallocating partitions to a first portion of potentially relevantbuckets, and allocating partitions to additional buckets from thepotentially relevant buckets as the partitions complete intake ofbuckets from the first portion).

The above interactions generally discuss information that is storedwithin common storage 4602. However, because the information in commonstorage 4602 is generated by indexers 206, searching of common storage4602 may be undesirable in instances in which search results are desiredimmediately. Specifically, where information from a data source 202 isrequired to pass through a forwarder 204, be processed at an indexer206, and stored in common storage 4602 before searching of thatinformation can be conducted by a worker node 3306, a significant delay(e.g., 2-4 minutes) may occur between generation of the information atthe data source 202 and searching of the information by a worker node3306. Thus, in the architecture of FIG. 46 , the indexers 206 may beconfigured to enable searching of information received at an indexer 206(prior to processing of that information and storage in the commonstorage 4602), in a manner similar to that described above withreference to FIG. 39 . However, utilization of the indexers 206 toconduct searching of not-yet-indexed information may incur some of thedisadvantages described above, such as the comingling of computingresources used to ingest information with resources used to searchinformation. It may therefore be desirable to provide an architecturethat enables worker nodes 3306, rather than indexers 206, to searchnot-yet-indexed information, without inhibiting operation of theindexers 206.

23.0. Ingested Data Buffer Architecture

One embodiment of the system 3301 that enables worker nodes 3306 tosearch not-yet-indexed information is shown in FIG. 48 . Searching ofnot-yet-indexed information (e.g., prior to processing of theinformation by an indexer 206) may be beneficial, for example, whereinformation is desired on a continuous or streaming basis. For example,a client device 404 a may desire to establish a long-running (e.g.,until manually halted) search of data received at the data intake andquery system 3301, such that the client is quickly notified onoccurrence of specific types of information within the data, such aserrors within machine records. Thus, it may be desirable to conduct thesearch against the data as it enters intake and query system 3301,rather than waiting for the data to be processed by the indexers 206 andsaved into a data store 208.

The embodiment of FIG. 48 is similar to that of FIG. 46 , andcorresponding elements will not be re-described. However, unlike theembodiment of FIG. 46 , the embodiment of FIG. 30 includes an ingesteddata buffer 4802. The ingested data buffer 4802 of FIG. 30 operates toreceive information obtained by the forwarders 204 from the data sources202, and make such information available for searching to both indexers206 and worker nodes 3306. As such, the ingested data buffer 4802 mayrepresent a computing device or computing system in communication withboth the indexers 206 and the worker nodes 3306 via a communicationnetwork.

In one embodiment, the ingested data buffer 4802 operates according to apublish-subscribe (“pub-sub”) messaging model. For example, each datasource 202 may be represented as one or more “topics” within a pub-submodel, and new information at the data source may be represented as a“message” within the pub-sub model. Elements of the system 3301,including indexers 206 and worker nodes 3306 (or partitions withinworker nodes 3306) may subscribe to a topic representing desiredinformation (e.g., information of a particular data source 202) toreceive messages within the topic. Thus, an element subscribed to arelevant topic will be notified of new data categorized under the topicwithin the ingested data buffer 4802. A variety of implementations ofthe pub-sub messaging model are known in the art, and may be usablewithin the ingested data buffer 4802. As will be appreciated based onthe description below, use of a pub-sub messaging model can provide manybenefits to the system 3301, including the ability to search dataquickly after the data is received at the ingested data buffer 4802(relative to waiting of the data to be processed by an indexer 206)while maintaining or increasing data resiliency.

In embodiments that utilize an ingested data buffer 4802, operation ofthe indexer 206 may be modified to receive information from the buffer4802. Specifically, each indexer 206 may be configured to subscribe toone or more topics on the ingested data buffer 4802 and to thereafterprocess the information in a manner similarly to as described above withrespect to other embodiments of the system. After data representing amessage has been processed by an indexer 206, the indexer 206 can sendan acknowledgement of the message to the ingested data buffer 4802. Inaccordance with the pub-sub messaging model, the ingested data buffer4802 can delete a message once acknowledgements have been received fromall subscribers (which may include, for example, a single indexer 206configured to process the message). Thereafter, operation of the system3301 to store the information processed by the indexer 206 and enablesearching of such information is similar to embodiments described above(e.g., with reference to FIGS. 33 and 46 , etc.).

As discussed above, the ingested data buffer 4802 is also incommunication with the worker nodes 3306. As such, the data intake andquery system 3301 can be configured to utilize the worker nodes 3306 tosearch data from the ingested data buffer 4802 directly, rather thanwaiting for the data to be processed by the indexers 206. As discussedabove, a query can be received at the search head 360, processed at thesearch process master 3302, and passed to a query coordinator 3304 forexecution. The query coordinator 3304 may generate a DAG correspondingto the query, in order to determine sequences of search phases withinthe query. The query coordinator 3304 may further determine based on thequery whether any branch of the DAG requires searching of data withinthe ingested data buffer 4802. For example, the query coordinator 3304may determine that at least one branch of the query requires searchingof data within the ingested data buffer 4802 by identifying, within thequery, a topic of the ingested data buffer 4802 for searching. It willbe assumed for the purposes of described that at least one branch of theDAG requires searching of data within the ingested data buffer 4802, andas such, description will be provided for execution of such a branch.While interactions are described for executing a single branch of a DAG,these interactions may be repeated (potentially concurrently or inparallel) for each branch of a DAG that requires searching of datawithin the ingested data buffer 4802. As discussed above with referenceto FIG. 36 , executing a search representing a branch of a DAG caninclude a number of phases, such as an intake phase 3604, processingphase 3606, and collector phase 3608. It is therefore illustrative todiscuss execution of a branch of a DAG that requires searching of thecommon storage 4602 with reference to such phases. As also discussedabove, each phase may be carried out by a number of partitions, each ofwhich may correspond to a worker node 3306 (e.g., a specific worker node3306, processor within the worker node 3306, execution environmentwithin a worker node 3306, etc.). Particularly in the case of streamingor continuous searching, different instances of the phases may becarried out at least partly concurrently. For example, the processingphase 3606 may occur with respect to a first set of information whilethe intake phase 3604 occurs with respect to a second set ofinformation, etc. Thus, while the phases will be discussed in sequencebelow, it should be appreciated that this sequence can occur multipletimes with respect to a single query (e.g., as new data enters thesystem 3301), and each sequence may occur at least partiallyconcurrently with one or more other sequences. Moreover, because theingested data buffer 4802 can be configured to make messages availableto any number of subscribers, the sequence discussed below may occurwith respect to multiple different searches, potentially concurrently.Thus, the architecture of FIG. 48 provides a highly scalable, highlyresilient, high availability architecture for searching informationreceived at the system 3301.

When a branch requires searching within ingested data buffer 4802, thequery coordinator 3304 can select a partition (e.g., a processor withina worker node 3306) at random or according to a load-balancing algorithmto gather metadata regarding the topic specified within the query fromthe ingested data buffer 4802. Metadata regarding a topic may include,for example, a number of message queues within the ingested data buffer4802 corresponding to the topic. Each message queue can represent acollection of messages published to the topic, which may be time-ordered(e.g., according to a time that the message was received at the ingesteddata buffer 4802). In some instances, the ingested data buffer 4802 mayimplement a single message queue for a topic. In other instances, theingested data buffer 4802 may implement multiple message queues (e.g.,across multiple computing devices) to aid in load-balancing operation ofthe ingested data buffer 4802 with respect to the topic. The selectedpartition can determine the number of message queues maintained at theingested data buffer 4802 for a topic, and return this information tothe query coordinator.

Thereafter, the query coordinator 3304 can dynamically assign partitionsto conduct an intake phase 3604, by retrieving individual message queuesof the topic within the ingested data buffer 4802. In one embodiment,the query coordinator 3304 attempts to maximize parallelization of theintake phase 3604, by attempting to retrieve messages from the messagequeues with a number of partitions equal to the number of message queuesfor the topic maintained at the ingested data buffer 4802 (e.g.,resulting in a one-to-one mapping of message queues in the topic topartitions). However, such parallelization may not be feasible ordesirable, for example, where the total number of partitions is lessthan the number of message queues, where some partitions are processingother queries, or where some partitions should be left in reserve toprocess other queries. Accordingly, the query coordinator 3304 mayinteract with the workload advisor 3310 to determine a number ofpartitions that are to be utilized to intake messages from the messagequeues during the intake phase 3604. Illustratively, the querycoordinator 3304 may initially request a one-to-one correspondencebetween message queues and partitions, and the workload advisor 3310 mayreduce the number of partitions used to read the message queues,resulting in a 2-to-1, 3-to-1, or n-to-1 correspondence between messagequeues and partitions. Operation of the workload advisor 3310 isdescribed in more detail above. When a greater than 1-to-1correspondence exists between queues and partitions (e.g., 2-to-1,3-to-1, etc.), the message queues may be evenly assigned among differentworker nodes 3306 used to implement the intake phase 3604, to maximizenetwork or read/write bandwidth available to partitions conducting theintake phase 3604.

During the intake phase 3604, each partition used during the intakephase 3604 can subscribe to those message queues assigned to thepartition. Illustratively, where partitions are assigned in a 1-to-1correspondence with message queues for a topic in the ingested databuffer 4802, each partition may subscribe to one corresponding messagequeue. Thereafter, in accordance with the pub-sub messaging model, thepartition can receive from the ingested data buffer 4802 messagespublishes within those respective message queues. However, to ensuremessage resiliency, a partition may decline to acknowledge the messagesuntil such messages have been fully searched, and results of the searchhave been provided to a data destination (as will be described in moredetail below).

In some embodiments, a partition may, during the intake phase 3604 actas an aggregator of messages published to a respective message queue ofthe ingested data buffer 4802, to define a collection of data to beprocessed during an instance of the processing phase 3606. For example,the partition may collect messages corresponding to a given time-window(such as a 30 second time window, 1 minute time window, etc.), andbundle the messages together for further processing during a processingphase 3606 of the search. In one instance, the time window may be set toa duration lower than a typical delay needed for an indexer 206 toprocess information from the ingested data buffer 4802 and place theprocessed information into a data store 208 (as, if a time-windowgreater than this delay were used, a search could instead be conductedagainst the data stores 208). The time window may further be set basedon an expected variance between timestamps in received information andthe time at which the information is received at the ingested databuffer 4802. For example, it is possible the information arrives at theingested data buffer 4802 in an out-of-order manner (e.g., such thatinformation with a later timestamp is received prior to information withan earlier timestamp). If the actual delay in receiving out-of-orderinformation (e.g., the delay between when information is actuallyreceived and when it should have been received to maintain propertime-ordering) exceeds the time window, it is possible that the delayedinformation will be processed during a later instance of the processingphase 3606 (e.g., with a subsequent bundle of messages), and as such,results derived from the delayed information may be deliveredout-of-order to a data destination. Thus, a longer time-window canassist in maintaining order of search results. In some instances, theingested data buffer 4802 may guarantee time ordering of results withineach message queue (though potentially not across message queues), andthus, modification of a time window in order to maintain ordering ofresults may not be required. In still more embodiments, the time-windowmay further be set based on computing resources available at the workernodes 3306. For example, a longer time window may reduce computingresources used by a partition, by enabling a larger collection ofmessages to be processed at a single instance of the processing phase3606. However, the longer time window may also delay how quickly aninitial set of results are delivered to a data destination. Thus, thespecific time-window may vary across embodiments of the presentdisclosure.

While embodiments are described herein with reference to a collection ofmessages defined according to a time-window, other embodiments of thepresent disclosure may utilize additional or alternative collectiontechniques. For example, a partition may be configured to include nomore than a threshold number of messages or a threshold amount of datain a collection, regardless of a time-window for collection. As anotherexample, a partition may be configured during the intake phase 3604 notto aggregate messages, but rather to pass each message to a processingphase 3606 immediately or substantially immediately. Thus, embodimentsrelated to time-windowing of messages are illustrative in nature.

In some embodiments, the partitions, during the intake phase 3604 mayfurther conduct coarse filtering on the messages received during a giventime-window, in order to identify any messages not relevant to a givenquery. Illustratively, the coarse filtering may include comparison ofmetadata regarding the message (e.g., a source, source type, or hostrelated to the message), in order to determine whether the metadataindicates that the message is irrelevant to the query. If so, such amessage may be removed from the collection prior to the search processproceeding to the processing phase 3606. In one embodiment, the coarsefiltering does not include searching for or processing the actualcontent of a message, as such processing may be predicted to berelatively computing resource intensive.

After generating a collection of messages from a respective messagequeue, the search can proceed to the processing phase 3606, where one ormore partitions are utilize to search the messages for informationrelevant to the search query. Illustratively, the searching that occursduring the processing phase 3606 may be predicted to be more processor(e.g., CPU) intensive than that which occurred during the intake phase3604. As such, the number of partitions used to conduct the processingphase 3606 may vary from that of the intake phase 3604. For example,during or after the conclusion of the intake phase 3604, each partitionimplementing that phase 3604 may communicate to the query coordinator3304 information regarding the collections of messages received during agiven time-window (e.g., the number, size, or formatting of messages,etc.). The query coordinator 3304 may thereafter determine from thatinformation (e.g., based on interactions with the workload advisor 3310)the partitions to be used to conduct the processing phase 3606. In otherembodiments, the query coordinator 3304 may select partitions to be usedto conduct the processing phase 3606 prior to implementation of theintake phase 3604 (e.g., contemporaneously with selecting partitions toconduct the intake phase 3604). The partitions selected for conductingthe processing phase 3606 may include one or more partitions thatpreviously conducted the intake phase 3604. However, because theprocessing phase 3606 may be expected to be more resource intensive thanthe intake phase 3604 (e.g., with respect to use of processing cycles),the number of partitions selected for conducting the processing phase3606 may exceed the number of partitions that previously conducted theintake phase 3604. To minimize network communications, the additionalpartitions selected to conduct the processing phase 3606 may bepreferentially selected to be collocated on a worker node 3306 with apartition that previously conducted the intake phase 3604, such thatportions of buckets to be processed by the additional partitions can bereceived from a partition on that worker node 3306, rather than beingtransmitted across a network.

At the processing phase 3606, the partitions may parse the collectionsof messages generated during the intake phase 3604 in order to identifyinformation relative to a search. For example, the may parse individualmessages to identify specific lines or segments that contain valuesspecified within the search, such as one or more error types desired tobe located during the search. Where the search is conducted according tomap-reduce techniques, the processing phase 3606 can correspond toimplementing a map function. Where the search requires that results betime-ordered, the processing phase 3606 may further include sortingresults at each partition into a time-ordering.

The remainder of the search may be executed in phases according to theDAG determined by the query coordinator 3304. For example, where thebranch of the DAG currently being processed includes a collection node,the search may proceed to a collector phase 23301. The collector phase3608 may be executed by one or more partitions selected by the querycoordinator 3304 (e.g., based on the information identified during theprocessing phase 3606), and operate to aggregate information identifiedduring the processing phase 3606 (e.g., according to a reduce function).Where the processing phase 3606 represents a top-node of a branch of theDAG being executed, the information located by each partition during theprocessing phase 3606 may be transmitted to the query coordinator 3304,where any additional nodes of the DAG are completed, and search resultsare transmitted to a data destination 3616. These additional phases maybe implemented in a similar manner as described above, and they aretherefore not discussed in detail with respect to searches against acommon storage 4602.

Subsequent to these phases, a set of search results corresponding toeach collection of messages (e.g., as received during a time-window) maybe transmitted to a data destination. On transmission of suchinformation (and potentially verification of arrival of such informationat the data destination), the search head 360 may cause anacknowledgement of each message within the collection to be transmittedto the ingested data buffer 4802. For example, the search head 360 maynotify the query coordinator 3304 that search results for a particularset of information (e.g., information corresponding to a range oftimestamps representing a given time window) have been transmitted to adata destination. The query coordinator 3304 can thereafter notifypartitions used to ingest messages making up the set of information thatthe search results have been transmitted. The partitions can thenacknowledge to the ingested data buffer 300 receipt of the messages. Inaccordance with the pub-sub messaging model, the ingested data buffer4802 may then delete the messages after acknowledgement by subscribingparties. By delaying acknowledgement of messages until after searchresults based on such messages are transmitted to (or acknowledged by) adata destination, resiliency of such search results can be improved orpotentially guaranteed. For example, in the instance that an erroroccurs between receiving a message from the ingested data buffer 4802and search results based on that message being passed to a datadestination (e.g., a worker node 3306 fails, causing a copy of themessage maintained at the worker node 3306 to be lost), the querycoordinator 3304 can detect the failure (e.g., based on heartbeatinformation from a worker node 3306), and cause the worker node 3306 tobe restarted, or a new worker node 3306 to replace the failed workernode 3306. Because the message has not yet been acknowledged to theingested data buffer 4802, the message is expected to still exist withina message queue of the ingested data buffer 4802, and thus, therestarted or new worker node 3306 can retrieve and process the messageas described below. Thus, by delaying acknowledgement of a message,failures of worker nodes 3306 during the process described above can beexpected not to result in data loss within the data intake and querysystem 3301.

In some embodiments, the ingested data buffer 4802 and searchfunctionalities described above may be used to make “enhanced” orannotated data available for searching in a streaming or continuousmanner. For example, search results may in some instances be representedby codes or other machine-readable information, rather than in aneasy-to-comprehend format (e.g., as error codes, rather than textualdescriptions of what such a code represents). Thus, the embodiment ofFIG. 48 may enable a client to define a long-running search that locatescodes within messages of the ingested data buffer 4802 (e.g., viaregular expression or other pattern matching criteria), correlates thecodes to a corresponding textual description (e.g., via a mapping storedin common storage 4602), annotates or modifies the messages to includerelevant textual descriptions for any code appearing within the message,and re-publishes the messages to the ingested data buffer 4802. In thismanner, the information maintained at the ingested data buffer 4802 maybe readily annotated or transformed by searches executed at the system3301. Any number of types of processing or transformation may be appliedto information of the ingested data buffer 4802 to produce searchresults, and any of such search results may be republished to theingested data buffer 4802, such that the search results are themselvesmade available for searching.

As will be appreciated in view of the above description, the use of aningested data buffer 4802 can provide many advantages within the dataintake and query system 3301. Specifically, use of a ingested databuffer 4802 can enable the system 3301 to utilize worker nodes 3306 tosearch not-yet-indexed information, thus decoupling searching of suchinformation from the functionality of data ingestion, as implemented byindexers 206. Moreover, because the ingested data buffer 4802 can makemessages available to both indexers 206 and worker nodes 3306, searchingof not-yet-indexed information by worker nodes 3306 can be expected notto detrimentally effect the operation of the indexers 206. Stillfurther, because the ingested data buffer 4802 can operate according toa pub-sub messaging model, the system 3301 may utilize selectiveacknowledgement of messages (e.g., after indexing by an indexer 206 andafter delivery of search results based on a message to a datadestination) to increase resiliency of the data on the data intake andquery system 3301. Thus, use of an ingested data buffer 4802 cansubstantially improve the speed, efficiency, and reliability ofoperation of the system 3301.

24.0. Ingested Data Buffer Flow

FIG. 49 is a flow diagram illustrative of an embodiment of a routine4900 implemented by the query coordinator 3304 to execute a query ondata from an ingested data buffer 4802. Although described as beingimplemented by the query coordinator 3304, one skilled in the relevantart will appreciate that the elements outlined for routine 4900 can beimplemented by one or more computing devices/components that areassociated with the system 3301, such as the search head 360, searchprocess master 3301, indexer 206, and/or worker nodes 3306. Thus, thefollowing illustrative embodiment should not be construed as limiting.

At block 4902, the query coordinator 3304 receives a query, as describedin greater detail above with reference to block 3802 of FIG. 38 . Atblock 4904, the query coordinator identifies the ingested data buffer4802 as a data source for the query (e.g., based on parameters of thequery, based on timing requirements as described in greater detail abovewith reference to block 3902 of FIG. 39 , etc.).

At block 4906, the query coordinator 3304 determines a set of messagequeues on the ingested data buffer 4802 to which messages potentiallyrelevant to the query are published. The message queues may bedetermined, for example, by querying the ingested data buffer 4802 basedon a topic specified within the query. In one embodiment, the querycoordinator 3304 may utilize a partition of a worker node 3306 toretrieve identifying information for the message queues from theingested data buffer 4802. In another embodiment, the query coordinator3304 may directly query the ingested data buffer 4802 for theidentifying information of the message queues.

At block 4908, the query coordinator 3304 allocates partitions toconduct windowed-intake of messages from message queues assigned to thepartitions. As described above, the query coordinator 3304 can allocatepartitions based on a number of factors, including a number of messagequeues to which potentially relevant messages are posted, a number ofpartitions available to intake the buckets, or a distribution ofpartitions across different worker nodes 3306 (e.g., to maximize anavailability of network or disk read/write bandwidth). In someembodiments, the query coordinator 3304 may interact with the workloadadvisor 3310 to allocate partitions to intake messages from messagequeues. In general, partitions may be allocated to intake potentiallyrelevant buckets in a manner that maximizes parallelization of retrievalof messages from message queues on the ingested data buffer 4802. Asnoted above, each partition may function to collect messages from itsrespective message queue during a given time-window (such as a 30 secondtime window, 1 minute time window, etc.), and bundle the messagestogether for further processing during a processing phase 3606 of thesearch. The time-window may be selected based on a number of factors, asdescribed in more detail above.

At block 4910, the query coordinator 3304 executes the query asdescribed in greater detail above with reference to FIGS. 36 and 48 . Itwill be understood that fewer, more, or different blocks can be used aspart of the routine 4700. For example, in some embodiments, the routine4700 can further include allocating partitions to conduct subsequentphases of a query, such as a processing phase 3606 or collection phase3608. As another example, in certain embodiments, the identification ofthe ingested data buffer 4802, determination of message queuescontaining potentially relevant messages, and allocation of partitionsto perform an intake phase 3604 can form part of a processing queryblock, similar to the process query block 3804 of FIG. 38 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 47 can be implemented in a variety oforders. In some cases, the system 3301 can implement some blocksconcurrently or change the order as desired. For example, the system3301 can in some instances allocate partitions to intake potentiallyrelevant messages from message queues dynamically. For example, thesystem 3301 may periodically or in response to information received fromthe ingested data buffer 4802 determine that the number of messagequeues containing potentially relevant messages has changed, and alterthe allocation of partitions to those message queues accordingly. 25.0.Distributed Execution Model Overview

Upon receipt of a query, one or more components of the system(s)described herein, such as the search head 210, search process master3302, or query coordinator 3304 can generate one or more models based onthe received query. The search head 210, search process master 3302, orquery coordinator 3304 that generates the one or more models can also bereferred to herein as a model generator.

Each model can have varying levels of details related to execution ofthe query. For example, a first model, which may also referred bereferred to herein as a syntax model, can include commands thatcorrespond to the commands in the received query. One non-limitingexample of a syntax model is an abstract syntax tree (AST). The commandsin the syntax model, in some embodiments, can be implemented as commandnodes. Each command node in the syntax model can be associated withvarious tasks or computation operations, also referred herein as alambda, in order to complete or execute the respective command.

A second model generated by the system, which may also referred bereferred to herein as a distributed execution model, can include a moredetailed description of the various commands, phases, and layers of thequery. In some cases, the second model can include computer-executableinstructions that when executed, cause processors to execute the query.In some embodiments, the second model can be generated by the systemusing the first model. For example, each command node in the syntaxmodel can be used to identify one or more lambdas for executing tasksassociated with the command. In addition, the model generator canidentify additional lambdas to initialize or prepare the processors ornodes for executing the query.

As the system generates the second model, it can access the relevantlambdas, such as lambdas associated with commands or lambdas toinitialize or prepare the processor for executing the commands, andinclude them in the second model. Once the second model is completed, itcan be distributed to one or more processors or nodes in the system,such as nodes 14, 214, 3306, or indexers 206, for execution. Thedistributed execution model can include sufficient instructions toenable the nodes or processors to communicate with each other to executethe query. One non-limiting embodiment of the second model is a directedacyclic graph (DAG), described above.

The queries received by the system can include a variety of commands,which are used to generate the different models. The commands caninclude map commands, reduce commands, and expand commands. Map commandsgenerally operate on a set of results or partitions. Reduce commandsgenerally reduce a set of results to a smaller set of results, which canresult in fewer partitions being used. Expand commands generallyincrease a set of results to a larger set of results, which can resultin more partitions being used.

Some of the commands can be trusted commands and others may be untrustedcommands. The trusted commands can correspond to commands createdinternally or that have otherwise reviewed and determined that they,their associated libraries, dependencies, dynamic-linked lists, or thecompilation of the files used to generate them would not interfere withthe execution of other commands. Examples of trusted commands in theSPLUNK® ENTERPRISE system include, but are not limited to, join, stats,count, search, countby, sort, etc. The untrusted commands can correspondto commands created by third parties, or other commands for which thesystem does not have all of the details of creation, execution,dependencies, etc. For example, the untrusted command may correspond toa command that has not been completely reviewed for potential errors,inconsistencies, processor and memory use, etc.

In some embodiments, to use the untrusted commands, a node that is toexecute an untrusted command can generate a new process or thread andexecute the tasks associated with the untrusted command using the newprocess or thread. However, executing the command in this way can resultin additional memory or timing requirements to execute the query. Incertain embodiments, the node can execute the untrusted command similarto the way in which it executes trusted commands. However, in executingthe untrusted command using in the same way as trusted commands, thesystem can expose itself to potential errors in the untrusted command,potential security risks, potential conflicts with other commands andtheir dependencies, etc.

In some cases, to use untrusted commands, the system can store relevantinformation about the untrusted commands in one or more files andgenerate a data structure using the file(s). The file(s) can includecomputer-executable instructions associated with the untrusted command,libraries and dependent libraries associated with the command,configuration files associated with the command, etc. Similarly, thesystem can store relevant information about trusted commands, and usethat information to generate, or access data structures, lambdas, orcomputation operations for inclusion in a distributed execution model.

For the untrusted commands, the generated data structure can be used toobtain an identifier associated with the tasks to be included in adistributed execution model, or to execute tasks associated with thecommand at a worker node. In this way, the untrusted commands can be inthe same process as the trusted commands, thereby improving theintegration of the untrusted commands with the trusted commands and thespeed of execution.

Furthermore, in certain embodiments, the system can generate or use anexecution environment, such as an isolated or restricted computingenvironment to interact with the file(s). Non-limiting examples ofrestricted execution environments, include, but are not limited to,software containers, sandboxes, virtual computing environments, etc. Theuse of the restricted computing environment can improve security relatedto the use of the untrusted command by reducing the likelihood ofconflicting or overlapping dependencies, dependent libraries, etc., andrestricting access to the underlying hardware or processes, etc.

25.1. Example Distributed Execution Model

As a non-limiting, consider the following query:

search index=airlinesdata|stats count by FlightNum,AirTime\extrancrepeats 2|extrana count by FlightNum|sort−count|head 10

The query indicates that the content of the index airlinesdata is to besearched, and the contents are to be counted based on the fieldsFlightNum and AirTime. The results of the count are to be furtherprocessed using the command extranc, and those results are to be furtherprocessed using the command extrana. The results of the extrana commandare to be sorted based on count and the top 10 results are to bedisplayed. In some cases, the stats, extrana, and head command can bereduce commands and the extranc can be an expand command.

Using a lookup table or registry, a model generator, such as the searchhead 210, search process master 3302, or query coordinator 3304, canidentify “extranc” and “extrana” as untrusted commands and “search,”“stats,” “sort,” and “head” as trusted commands. The model generator canidentify the untrusted commands by parsing the query itself, or byparsing a syntax model generated using the query. For example, thesyntax model can include a command node for each of the above identifiedcommands, such as a search command node, a stats command node, a sortcommand node, ahead command mode, an extrana command node, and extranccommand node. In some cases, the model generator can use a dedicatedcommand node to identify untrusted commands. Thus, when parsing thesyntax model, the model generator can identify untrusted commands basedon the dedicated command node.

To generate the distributed execution model, the model generator canparse the syntax model, or the query, and identify or generatecomputation tasks associated with each command of the query or commandnode of the syntax model. The model generator can combine the variouscomputation tasks into the distributed execution model. For example, themodel generator can identify multiple computation tasks to execute thestats command node. These “stats command” computation tasks can beincluded in the distributed execution model. In some cases, the modelgenerator can use one or more libraries, DLLs, configuration files,etc., to generate the computation tasks associated with the statscommand (or other trusted commands) for inclusion in the distributedexecution model. In this way, the model generator can transform eachcommand from the query or each command node from the syntax model intocomputation tasks that when executed complete the command.

As extrana and extranc are untrusted commands, which may haveoverlapping or conflicting dependencies or configurations, the modelgenerator can include identifiers corresponding to the computation tasksin the distributed execution model rather than the computation tasksthemselves. The model generator can obtain the identifiers in a varietyof ways.

In some embodiments, to obtain the identifiers, one or more modelingprocess(es) executing on the model generator can communicate dataassociated with the extrana and extranc commands to a restrictedcomputing environment. This data can include an identifier, such as afile name or file location, for file(s) associated with the extrana andextranc commands. Using the received information, the restrictedcomputing environment can access the file(s) and use the file(s) togenerate a data structure, such as a computer object. The restrictedcomputing environment can communicate the data structure back to themodeling process(es). In turn, modeling process(es) can perform someadditional processing on the data structure and/or extract identifiersfor the one or more computation tasks associated with the command.

In certain embodiments, to obtain the identifiers, the modelingprocess(es) can use the data associated with the extrana and extranccommands to access the file(s), generate the data structure using thefile(s), further process the data structure and/or extract theidentifiers from the data structure.

Once the identifiers are obtained, the modeling process(es) can includethe identifiers in the distributed execution model. In some cases, theidentifiers can be associated with a command used to identify untrustedcommands. For example, the modeling process(es) can use a dedicatedcommand, such as “load( )” which uses one or more of the identifiers asa parameter (e.g., load(untrustedCommandIdentifier)).

Upon completion of the distributed execution model, the model generator,or other component of the system, can communicate the distributedexecution model to one or more nodes, such as nodes 14, 214, 3306, orindexers 206, that are assigned to execute the distributed executionmodel. In addition, the model generator, or other component of thesystem, can communicate the file(s) associated with the untrustedcommands, as well as similar files associated with trusted commands thatare referenced in the distributed execution model, to the nodes.

As the distributed execution model is communicated to multiple nodesand, in some cases, designed to be executed by multiple nodes, each nodecan be assigned to execute a portion of the distributed execution model.Accordingly, the node or nodes assigned to execute the extrana orextranc command, can identify these commands as external commands, suchas, by using the identifier inserted by the model generator.

To execute the extrana or extranc command, the node can generate a datastructure similar to the way in which the data structure is generated bythe model generator. For example, one or more node processes used toexecute the distributed execution model can access the file(s) receivedfrom the model generator and generate the data structure using thefile(s), or the node can use its own restricted computing environment toaccess the file(s) and generate the data structure.

The node process(es) can further process the data structure and identifyexecutable portions of the data structure that correspond to thecomputation operations that, in the aggregate, result in the executionof the extrana or extranc command. The node process(es) can then executethe identified executable portions of the data structure in order toexecute the extrana or extranc command.

Conversely, for trusted commands, the node process(es) can execute thelambdas that were included by the model generator. In certainembodiments, the lambdas may refer to one or more libraries or DLLs,which can be accessed by the node to execute the associated lambda.However, in certain embodiments, the node process(es) can execute thetrusted commands without generating the data object or using arestricted computing environment.

25.2. Distributed Execution Model Generation with Untrusted Commands

FIG. 50 is a data flow diagram illustrating an embodiment ofcommunications between different processes within a component of thesystem, such as system 16, 108, 1006, 200, 202, 224, or 3301, or betweendifferent components of the system to generate a distributed executionmodel.

At 5002, one or more modeling processes 5001, such as one or moreprocesses executing on the model generator, receives and begins toprocess a query. As described above with reference to at least blocks3802 and 3804 of FIG. 38 , the query can be in a query language, such asSPL. As part of the processing, the modeling process(es) 5001 can parsethe query, generate a syntax model, and/or begin to generate adistributed execution model.

To generate the syntax model, the system can parse the query and includea command node in the syntax model for each of the commands in thequery. For example, if the query includes a stats and join command, thesyntax model can include a stats command node and a join command node.

Further, each command node in the syntax model can correspond to one ormore libraries, dependent libraries, dependencies, dynamic linked-lists(DLLs), computer-executable instructions, such as binary, etc. Thelibraries, DLLs and computer-executable instructions can be used togenerate computation operations, or tasks, to be included in thedistributed execution model in order to execute the command at a node,such as nodes 14, 524, 3306, or indexers 516. In some cases, acomputation operation can also be referred to as a lambda

Using the query and/or the syntax model, the system can generate thedistributed execution model. In some embodiments, to generate thedistributed execution model, the system can identify the configurationfiles, libraries, dependent libraries, dependencies, and DLLs associatedwith the command nodes in the syntax model. Using the identifiedinformation, the system can generate computation operations, or lambdas,and include the computation operations, or references thereto, in thedistributed execution model.

Furthermore, the system can generate and/or include additional lambdasthat enable the output of one command to be input into a proximatecommand. In this way, the model generator can stitch the variouscommands together in order to implement the query. In addition, themodel generator can generate and/or identify additional lambdas toinitialize or prepare a node to execute the distributed execution model.Such lambdas can include instructions to instantiate a restrictedcomputing environment 5003, as described herein, and so forth.

At 5004, the modeling process(es) 5001 can identify untrusted commands.The modeling process(es) 5001 can identify the untrusted commands fromthe query and/or the syntax model. This can be done by parsing the queryand/or the syntax model as part of processing the query as describedabove with reference to 5002.

In certain embodiments, the modeling process(es) 5001 can use a lookuptable, registry or other mechanism to identify untrusted commands, aswell as trusted commands or unknown commands. In some cases, if thecommand is an unknown command, the system can return an error,indicating that the unknown command is not recognized and/or cannot beincluded in the distributed execution model. In some embodiments, thesyntax model can include a particular command node for identifyinguntrusted commands. Accordingly, upon identifying the particular commandnode, the modeling process(es) 5001 can determine that an untestedcommand is to be performed.

In some cases, the model generator can use internal transformers toprocess the trusted commands. The internal transformers can be used togenerate or identify the respective lambdas from the files associatedwith the internal command and insert the lambdas into the distributedexecution model. In some cases, the internal transformer can be used totransform the trusted commands into one or more computation operations,which can be included in the distributed execution model.

As shown at 5006, for an untrusted command, the modeling process(es)5001 can communicate data associated with the untrusted command to arestricted computing environment 5003. For example, the modelingprocess(es) 5001 can communicate a file name and file location of one ormore files associated with the untrusted command. As described above,these files can include computer executable instructions, such asbinary, configuration files, libraries, dependent libraries,dependencies, DLLs, etc., that can be used to generate a data structureand identify the lambdas or tasks to perform the command.

In some cases, the modeling process(es) 5001 can communicate theidentifying information using an additional process, such as an externaltransformer or an external command loader. In certain embodiments, theexternal transformer can be used to process the untrusted commands forinsertion into the distributed execution model. In some cases, theexternal transformer can be used to transform the untrusted commandsinto one or more computation operations. The computation operations, orreferences thereto, can be included in the distributed execution model.In some embodiments, the external transformer can be more restrictivethan internal transformers to prevent potential errors caused byuntrusted commands, or otherwise be designed specifically for use withuntrusted commands.

Further, the various internal and external transformers used to generatedifferent portions of the distributed execution model can be performedsequentially or in parallel. For example, the system can initiate aninternal transformer for each of the trusted commands concurrently withan extra transformer for each of the untrusted commands. In this way,the system can generate the distributed execution model in parallel.

As mentioned above, the restricted computing environment 5003 can beimplemented using any one or any combination of, a software container, asandbox, or virtual computing environment, etc., or other computingenvironment that provides additional restrictions on processes executedtherein to prevent the processes from affecting the underlying hardware,operating system, or processes. Upon receipt of the identifyinginformation, the restricted computing environment 5003 can access one ormore files associated with the untrusted command as illustrated at 5008.

The files associated with the untrusted command can include computerexecutable instructions, such as binary, configuration files, libraries,dependent libraries, dependencies, DLLs, etc. The files can also includeone or more commands to generate a data structure using the files. Thefiles can be stored at a location accessible by the modeling process(es)5001, such as at the model generator or other component of the system.In addition, the files can be stored in a permanent directory 5005 ofthe system, or non-temporary directory 5005.

At 5010, using the accessed one or more files associated with theuntrusted command, a process in the restricted computing environment5003 can generate a data structure, such as a computer object. The datastructure or object can include information about a memory location withdata, executable instructions, such as binary, class information, etc.In certain embodiments, the data structure or object can be an object ofa programming language, such as, but not limited to, C++, python, scala,etc.

In some cases, the restricted computing environment 5003 generates thedata structure based on a command found in one of the accessed files.For example, a file that includes binary associated with the untrustedcommand can include a command that indicates that the file and otherfiles associated with the untrusted command are to be used to generate adata structure.

At 5012, the restricted computing environment 5003 can communicate thedata structure to the modeling process(es) 5001 (inclusive of theexternal transformer). At 5014, the modeling process(es) 5001 canperform additional process on the received data structure. For example,the modeling process(es) 5001 can caste the data structure and extractidentifiers for one or more portions of the data structure, such as oneor more lambdas used to execute the command.

At 5016, the modeling process(es) 5001 (inclusive of the externaltransformer) can append the identifiers to the one or more lambdas tothe distributed execution model. In some cases, the identifiers areincluded as part of a command in the distributed execution model.Accordingly, for a single untrusted command, the distributed executionmodel can include one or more commands that reference differentidentifiers associated with the untrusted command and/or are associatedwith different portions of the generated data structure.

For each untrusted command, the system can repeat steps 5004-5016. Inthis way, the distributed execution model can include commandsassociated with multiple untrusted commands from the query or syntaxmodel. Furthermore, as mentioned above, the system can includeadditional lambdas that enable the output of an untrusted command to bean input to trusted or untrusted command and vice versa. In this way,when the distributed execution model is executed by a node, it canexecute the various commands assigned to it as part of a single process.

Although not illustrated in FIG. 50 , the model generator or othercomponent of the system can distribute the distributed execution modelto multiple nodes for execution. In addition, the model generator orother component of the system can communicate the files relevant to theuntrusted commands to the nodes. The files communicated to the nodes cancorrespond to the files accessed to generate the data structurecorresponding to the untrusted command, as described above. The nodescan store the received files in one or more directories for use whenexecuting the distributed execution model. In some cases, the nodes canstore the received files in a temporary folder, which can be removedonce the distributed execution model has been executed. In addition, insome embodiments, the model the model generator or other component ofthe system can communicate similar files for trusted commands that arereferenced by the distributed execution model.

In addition, it will be understood that the model generator can generatethe distributed execution model in a variety of ways, for example, byomitting or combining some of the steps illustrated in FIG. 50 . In somecases, the model generator can generate the distributed execution modelfrom the query without first generating the syntax model. In suchembodiments, the model generator can identify commands in the query, usethe identified commands to identify one or more relevant files for thecommands, and generate lambdas or other commands to be included in thedistributed execution model using the identified files.

As yet another example, in certain embodiments, the model generator cangenerate the distributed execution model without a restricted computingenvironment 5003. In such embodiments, the modeling process(es) 5001 canuse the untrusted command information to access the relevant files fromthe directory 5005, and construct the data structure using the files,rather than relying on the restricted computing environment 5003 toperform these steps.

FIG. 51 is a flow diagram illustrative of an embodiment of a routine5100 implemented by the system to generate a distributed executionmodel. For simplicity purposes, reference will be made to executing theblocks using a modeling process executing on a model generator. However,one skilled in the relevant art will appreciate that the elementsoutlined for routine 5100 can be implemented by one or more processesexecuting on one or more computing devices/components that areassociated with the system, such as the search head 210, search processmaster 3302, query coordinator 3304, etc. Thus, the followingillustrative embodiment should not be construed as limiting.

At block 5102, the modeling process receives a query. As described ingreater detail above at least with reference to block 3802 of FIG. 38 ,the modeling process can receive the query in a variety of ways.

At block 5104, the modeling processes the query. As described above, themodeling process can process the query in a variety of ways. Forexample, the modeling process can parse the query to identify one ormore commands in the query, and generate a syntax model based on theidentified commands or parsing. The query processing can also includegenerating a distributed execution model based on the commands in thequery and/or the syntax model. In addition, the query processing caninclude processing the query as described above at least with referenceto block 3804 of FIG. 38 . Furthermore, it will be understood that insome embodiments, blocks 5106-5114 can be included as part of processingthe query.

At block 5106, the modeling process identifies an untrusted command. Asdescribed above, the modeling process can identify untrusted commands inthe query and/or in the syntax model.

At block 5108, the modeling process communicates data associated withthe untrusted command data to a restricted computing environment. Asdescribed above, the data associated with the untrusted command caninclude information that enables the restricted computing environment toidentify relevant files that can be used to generate a data structure.In some embodiments, the untrusted command data can include the namesand locations of the relevant files. As described above, the relevantfiles can include, but are not limited to, libraries, dependentlibraries, dependencies, DLLs, binary files, configuration files, andthe like.

In some embodiments, the modeling process can communicate the untrustedcommand data to the restricted computing environment using one or moreadditional processes, such as an external command loader, or an externaltransformer. Further, the data associated with the untrusted commanddata can be obtained by the modeling process by accessing one or moreregistries storing information regarding untrusted commands that can beused on the system.

Using the untrusted command data, the restricted computing environmentcan access the relevant files and generate the data structure. Bygenerating the data structure in the restricted computing environment,the system enables the untrusted command to use its own libraries,dependent libraries, dependencies, configuration files, and DLLs withoutregard to potential conflicts with other trusted or untrusted commands.

For example, in some cases, because the system is familiar with thevarious constraints, libraries, and dependencies of the trusted commandsit can generate the data structures for those commands while avoidingpotential conflicts. With respect to untrusted commands, the system maynot be aware of the particular libraries or dependencies used togenerate the data structures. As such, generating the data structureusing the untrusted commands can result in conflicting dependencies orother errors that may make the system unable to process the query.However, by generating the data structure for each untrusted command ina separate portion of the restricted data environment, the system canavoid errors created by conflicting libraries or dependencies.

In certain embodiments, one of the files can include a configurationfile that indicates how the data structure is to be constructed, andwhat its contents will be. This can include identifying differentparameters that are to be passed to, used by, or returned from the datastructure.

In some embodiments, one of the files can include an instruction togenerate the data structure. Accordingly, upon accessing that file, aprocess executing on the restricted computing environment can generatethe data structure. The instruction to generate the data structure, canprovide the restricted computing environment with information related towhat files to use to generate the data structure, how the files are tobe used to generate the data structure, and the details related to thedata structure that is created, such as its format, its name oridentifier, identifiers associated with portions of the data structure,etc. Once the data structure is generated, the restricted computingenvironment can communicate it to the modeling process.

At block 5110, the modeling process receives the data structure from therestricted computing environment. As mentioned above, in some cases, themodeling process can use an external command loader or an externaltransformer to receive the data structure.

At block 5112, the modeling process processes the data structure. Insome embodiments, the modeling process can perform additionaltransformations or additional processing on the received data structure.For example, in some cases, the modeling process can caste the datastructure, extract identifiers of portions of the data structure fromthe data structure, and/or identify other portions of the datastructure.

At block 5114, the modeling process includes an identifier associatedwith the data structure in a distributed execution model. In some cases,the identifier can be included as part of the command in the distributedexecution model. This command can indicate that the identifierreferences at least a portion of a data structure associated with anuntrusted command.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 5100. For example, the model generator caninclude a block for generating a syntax model and parsing the syntaxmodel to identify the untrusted commands. In some cases, the routine5100 can further include a block for the model generator to provide thedistributed execution model to one or more nodes. In addition, the modelgenerator can communicate one or more of the files used to generate aparticular data structure to the nodes, one or more files referred to bythe distributed execution model, such as libraries, DLLs, dependencies,etc., that are referenced by the distributed execution model, which mayor may not correspond to trusted or untrusted commands. For example,some lambdas in the distributed execution model that correspond totrusted commands may reference one or more libraries or DLLs, which canbe communicated to the nodes. In some cases files additional files canbe communicated in a manner different from the way in which thedistributed execution model is communicated. For example, whereas thedistributed execution model can be identified by the model generator forexecution, the model generator can identify the files as non-executableand/or for storage in a temporary directory of the nodes. Furthermore,the routine 5100 can include a block for instantiating the restrictedcomputing by environment.

In some embodiments, blocks 5110 and 5112 can be replaced with themodeling process receiving the identifiers of the portions of the datastructure. In such embodiments, the restricted computing environment canextract the identifiers from the data structure it created andcommunicate the identifiers to the modeling process rather than theFurther, in certain embodiments, at block 5112, the modeling process canextract one or more executable portions of the data structure. At block5114, the modeling process can insert the executable portions of thedata structure into the distributed execution model or include theexecutable portions of the data structure in one or more files that arecommunicated to the nodes separately from the distributed executionmodel. In this way, the system can avoid sending the files correspondingto the untrusted command to the nodes, and the nodes can execute theuntrusted command without using their own restricted computingenvironment and/or without generating their own data structure. However,in certain embodiments, this may increase the likelihood of potentialconflicts between libraries and commands, etc.

In some cases, one or more blocks can be omitted. For example, blocks5108 and 5110 can be omitted. In place thereof, the modeling process canaccess files associated with the untrusted command, and use the files togenerate a data structure. In some embodiments, the modeling process canalso include an ordering for the data structure identifiers, which canbe used to include the identifiers in the distributed execution model ina particular order.

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 51 can be implemented in a variety oforders. In some cases, the model generator can implement some blocksconcurrently or change the order as desired. In some cases, the modelgenerator can identify untrusted commands, while it concurrentlygenerates the distributed execution model. For example, upon identifyinga first untrusted command, the model generator can communicate the firstuntrusted command to the restricted computing environment, receive adata structure corresponding to the first untrusted command from therestricted computing environment, process the data structure, andinclude an identifier associated with the data structure in thedistributed execution model, while concurrently continuing to identifyand process additional untrusted commands, or process trusted commands.Accordingly, the model generator can generate portions of thedistributed execution model in a parallelized fashion.

25.3. Executing a Distributed Execution Model with Untrusted Commands

FIG. 52 is a data flow diagram illustrating an embodiment ofcommunications between different processes within a component of thesystem, or between different components of the system, to execute atleast a portion of a distributed execution model such as an untrustedcommand referenced by the distributed execution model.

At 5202, one or more node processes 5201, such as one or more processes5201 executing on a worker node 14, 214, 3306, or indexer 206, receivethe distributed execution model. As described above, the distributedexecution model can include a variety of commands to execute a queryacross one or more nodes. Accordingly, one node that receives thedistributed execution model can implement a portion of the distributedexecution model in order to implement a portion of the query. Asdescribed in greater detail above, the nodes can communicate with eachother to determine which nodes will execute which parts of thedistributed execution model.

In some embodiments, the node process(es) 5201 can begin to execute thedistributed execution model. For example, as mentioned above, thedistributed execution model can include one or more computinginstructions to initialize the worker node to execute the distributedexecution model. For example, the distributed execution model caninclude one or more lambdas that cause the worker node to generate arestricted computing environment 5203 to handle untrusted commands orperform other initialization lambdas, such as reinitializing search heador query coordinator metadata, prior to executing the commands of thedistributed execution model.

At 5204, the node process(es) 5201 can identify untrusted commands. Insome embodiments, the node process(es) 5201 can identify an untrustedcommand based on a particular command or lambda in the distributedexecution model that identifies an untrusted command. For example, thedistributed execution model can include a function call that is used forall untrusted commands. Accordingly, upon encountering such a functioncall or command, the node process(es) 5201 can determine that anuntrusted command is to be executed. In certain embodiments, the nodeprocess(es) 5201 can identify an untrusted command using a lookup tableor registry as described in greater detail above.

As shown at 5206, for an untrusted command, the node process(es) 5201can communicate data associated with the untrusted command to arestricted computing environment 5203. It will be understood, that incertain embodiments, the restricted computing environment 5203 used bythe node process(es) 5201 can be different from the restricted computingenvironment 5003 used by the modeling process(es) 5001. For example, therestricted computing environment 5003 associated with the modelingprocess(es) 5001 can be instantiated on the model generator, whereas therestricted computing environment 5203 associated with the nodeprocess(es) 5201 can be instantiated on a node.

As described above, the node process(es) 5201 can communicate a filename and file location of one or more files associated with theuntrusted command to the restricted computing environment 5203. Asfurther described above, these files can include computer executableinstructions, such as binary, configuration files, libraries, dependentlibraries, dependencies, DLLs, etc., that can be used to generate thelambdas or tasks to perform the command. In some embodiments, the datacommunicated and the manner in which the data is communicated to therestricted computing environment 5203 can be similar to the data and themanner in which the data is communicated to the restricted computingenvironment 5003. In certain embodiments, the data communicated and themanner in which the data is communicated can be different. For examplein certain embodiments, the modeling process(es) 5001 can use anadditional process, such as an external transformer, to communicate thedata to the restricted computing environment 5003, whereas, the nodeprocess(es) 5201 may not use an additional process to communicate thedata to the restricted computing environment 5203.

As described herein, the restricted computing environment 5203 can beimplemented using any one or any combination of, a software container, asandbox, virtual computing environment, etc. Furthermore, as describedabove with reference to FIG. 50 , the restricted computing environment5203 can use the received data to access one or more files associatedwith the untrusted command, as illustrated at 5208, and generate a datastructure, as illustrated at 5210. As described above, by generating thedata structure using the restricted computing environment 5203, the nodecan reduce the likelihood of conflicts between libraries, variables,dependencies, of the different untrusted commands, and between anyuntrusted commands and trusted commands.

The files associated with the untrusted command can be similar to thefiles described above with reference to 5008 of FIG. 50 . In someembodiments, where the files referenced above at 5008 are storedpermanently or for multiple uses, the files used to generate the datastructure at 5208 can be stored in a temporary directory 5205, directory5205 designated for deletion upon completion of the distributedexecution model, or other directory 5205.

At 5212, the birth restricted computing environment 5203 can communicatethe data structure to the node process(es) 5201. At 5214, the nodeprocess(es) 5201 can perform additional process on the received datastructure. For example, the node process(es) 5201 can caste the datastructure and extract identifiers for one or more portions of the datastructure.

At 5216, the node process(es) 5201 can execute at least a portion of thedata structure. In some cases, the portion of the data structureexecuted can correspond to the identifiers extracted from the datastructure. In some cases, the portions of the data structure executedcan correspond to executable sub-objects of a computer object.

It will be understood, that the node can perform fewer or moreoperations as desired. For example, in some embodiments, for eachuntrusted command, the node can repeat steps 5204-5216. Further, it willbe understood that the node can execute the distributed execution modelin a variety of ways, for example, by omitting or combining some of thesteps illustrated in FIG. 52 . For example, in certain embodiments, thenode can execute the distributed execution model without the use ofrestricted computing environment 5203. In such embodiments, the nodeprocess(es) 5201 can use the untrusted command information to access therelevant files from the directory 5205, and construct the data structureusing the files, rather than relying on the restricted computingenvironment 5203 to perform these steps.

FIG. 53 is a flow diagram illustrative of an embodiment of a routine5300 implemented by a component of the system to execute a distributedexecution model. For simplicity purposes, reference will be made toexecuting the blocks using a node process. However, one skilled in therelevant art will appreciate that the elements outlined for routine 5300can be implemented by one or more processes executing one or morecomputing devices/components that are associated with the system, suchas one or more worker nodes. Thus, the following illustrative embodimentshould not be construed as limiting.

At block 5302, the node process receives a distributed execution model.As described above, in some embodiments as part of receiving thedistributed execution model, the node process can receive one or morefiles associated with trusted or untrusted commands, and store the oneor more files in a directory of the node. Furthermore, as part ofreceiving the distributed execution model, the node process can begin toexecute at least a portion of the distributed execution model. Forexample, the node process can instantiate the restricted computingenvironment.

At block 5304, the node process identifies an untrusted command. Asdescribed above, the node process can identify the untrusted commandbased on an identifier associated with the untrusted command and/or acommand in the distributed execution model that indicates an untrustedcommand is referenced therein. For example, a command, such asloadExternal (“externalCommand”) can be used to indicate that anuntrusted command is to be executed. Upon identifying this command, thenode process can parse it to identify the untrusted command. Forexample, “externalCommand” can be an identifier associated with theuntrusted command. In some embodiments, this identifier can correspondto the identifier inserted into the distributed execution model by themodeling process, as described above with reference to 5114 of FIG. 51 .

At block 5306, the node process communicates data associated with theuntrusted command data to a restricted computing environment. Asdescribed above, the data associated with the untrusted command caninclude information that enables the restricted computing environment toidentify relevant files that can be used to generate a data structure.In some embodiments, the untrusted command data can include the namesand locations of the relevant files. The relevant files can include, butare not limited to, libraries, dependent libraries, DLLs, binary files,configuration files, and the like.

As described in greater detail above with reference to block 5108 ofFIG. 51 , the data can be communicated in a variety of ways. Further,using the untrusted command data, the restricted computing environmentcan access the relevant files and generate the data structure, which canprovide a number of benefits.

At block 5310, the node process receives the data structure from therestricted computing environment. As mentioned above, in some cases, thenode process can use an external command loader or an externaltransformer to receive the data structure.

At block 5312, the node process processes the data structure. In someembodiments, the node process can perform additional transformations oradditional processing on the received data structure. For example, insome cases, the node process can caste the data structure, extractidentifiers of portions of the data structure from the data structure,and/or identify other portions of the data structure.

At block 5314, the node process executes at least a portion of the datastructure. In some embodiments, the node process can execute multipleportions of the data structure. For example, to execute the untrustedcommand the node process can execute multiple computation operations orlambdas that, in the aggregate, form the untrusted command.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 5300. For example, the node can include a blockfor receiving the files associated with interested commands. As anotherexample, the routine 5300 can include a block for instantiating therestricted computing by environment.

In some cases, one or more blocks can be omitted. For example, blocks5306 and 5308 can be omitted. In place thereof, the node process canaccess files associated with the untrusted command, and use the files togenerate a data structure. Furthermore, it will be understood that thevarious blocks described herein with reference to FIG. 53 can beimplemented in a variety of orders. In some cases, the node canimplement some blocks concurrently or change the order as desired. Insome cases, the node can identify untrusted commands concurrently.

Although FIGS. 50-53 have been described with reference to system 16,108, 1006, 200, 202, 224, or 3301, it will be understood that theconcepts described therewith can be used with a variety of distributedsystems.

26.0. Hardware Embodiment

FIG. 54 is a block diagram illustrating a high-level example of ahardware architecture of a computing system in which an embodiment maybe implemented. For example, the hardware architecture of a computingsystem 72 can be used to implement any one or more of the functionalcomponents described herein (e.g., indexer, data intake and querysystem, search head, data store, server computer system, edge device,etc.). In some embodiments, one or multiple instances of the computingsystem 72 can be used to implement the techniques described herein,where multiple such instances can be coupled to each other via one ormore networks.

The illustrated computing system 72 includes one or more processingdevices 74, one or more memory devices 76, one or more communicationdevices 78, one or more input/output (I/O) devices 80, and one or moremass storage devices 82, all coupled to each other through aninterconnect 84. The interconnect 84 may be or include one or moreconductive traces, buses, point-to-point connections, controllers,adapters, and/or other conventional connection devices. Each of theprocessing devices 74 controls, at least in part, the overall operationof the processing of the computing system 72 and can be or include, forexample, one or more general-purpose programmable microprocessors,digital signal processors (DSPs), mobile application processors,microcontrollers, application-specific integrated circuits (ASICs),programmable gate arrays (PGAs), or the like, or a combination of suchdevices.

Each of the memory devices 76 can be or include one or more physicalstorage devices, which may be in the form of random access memory (RAM),read-only memory (ROM) (which may be erasable and programmable), flashmemory, miniature hard disk drive, or other suitable type of storagedevice, or a combination of such devices. Each mass storage device 82can be or include one or more hard drives, digital versatile disks(DVDs), flash memories, or the like. Each memory device 76 and/or massstorage device 82 can store (individually or collectively) data andinstructions that configure the processing device(s) 74 to executeoperations to implement the techniques described above.

Each communication device 78 may be or include, for example, an Ethernetadapter, cable modem, Wi-Fi adapter, cellular transceiver, basebandprocessor, Bluetooth or Bluetooth Low Energy (BLE) transceiver, or thelike, or a combination thereof. Depending on the specific nature andpurpose of the processing devices 74, each I/O device 80 can be orinclude a device such as a display (which may be a touch screendisplay), audio speaker, keyboard, mouse or other pointing device,microphone, camera, etc. Note, however, that such I/O devices 80 may beunnecessary if the processing device 74 is embodied solely as a servercomputer.

In the case of a client device (e.g., edge device), the communicationdevices(s) 78 can be or include, for example, a cellulartelecommunications transceiver (e.g., 3G, LTE/4G, 5G), Wi-Fitransceiver, baseband processor, Bluetooth or BLE transceiver, or thelike, or a combination thereof. In the case of a server, thecommunication device(s) 78 can be or include, for example, any of theaforementioned types of communication devices, a wired Ethernet adapter,cable modem, DSL modem, or the like, or a combination of such devices.

A software program or algorithm, when referred to as “implemented in acomputer-readable storage medium,” includes computer-readableinstructions stored in a memory device (e.g., memory device(s) 76). Aprocessor (e.g., processing device(s) 74) is “configured to execute asoftware program” when at least one value associated with the softwareprogram is stored in a register that is readable by the processor. Insome embodiments, routines executed to implement the disclosedtechniques may be implemented as part of OS software (e.g., MICROSOFTWINDOWS® and LINUX®) or a specific software application, algorithmcomponent, program, object, module, or sequence of instructions referredto as “computer programs.”

27.0 Terminology

Computer programs typically comprise one or more instructions set atvarious times in various memory devices of a computing device, which,when read and executed by at least one processor (e.g., processingdevice(s) 74), will cause a computing device to execute functionsinvolving the disclosed techniques. In some embodiments, a carriercontaining the aforementioned computer program product is provided. Thecarrier is one of an electronic signal, an optical signal, a radiosignal, or a non-transitory computer-readable storage medium (e.g., thememory device(s) 76).

Any or all of the features and functions described above can be combinedwith each other, except to the extent it may be otherwise stated aboveor to the extent that any such embodiments may be incompatible by virtueof their function or structure, as will be apparent to persons ofordinary skill in the art. Unless contrary to physical possibility, itis envisioned that (i) the methods/steps described herein may beperformed in any sequence and/or in any combination, and (ii) thecomponents of respective embodiments may be combined in any manner.

Although the subject matter has been described in language specific tostructural features and/or acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as examples of implementing theclaims, and other equivalent features and acts are intended to be withinthe scope of the claims.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense, i.e., in the sense of “including, but notlimited to.” As used herein, the terms “connected,” “coupled,” or anyvariant thereof means any connection or coupling, either direct orindirect, between two or more elements; the coupling or connectionbetween the elements can be physical, logical, or a combination thereof.Additionally, the words “herein,” “above,” “below,” and words of similarimport, when used in this application, refer to this application as awhole and not to any particular portions of this application. Where thecontext permits, words using the singular or plural number may alsoinclude the plural or singular number respectively. The word “or” inreference to a list of two or more items, covers all of the followinginterpretations of the word: any one of the items in the list, all ofthe items in the list, and any combination of the items in the list.Likewise the term “and/or” in reference to a list of two or more items,covers all of the following interpretations of the word: any one of theitems in the list, all of the items in the list, and any combination ofthe items in the list.

Conjunctive language such as the phrase “at least one of X, Y and Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to convey that an item, term, etc. may beeither X, Y or Z, or any combination thereof. Thus, such conjunctivelanguage is not generally intended to imply that certain embodimentsrequire at least one of X, at least one of Y and at least one of Z toeach be present. Further, use of the phrase “at least one of X, Y or Z”as used in general is to convey that an item, term, etc. may be eitherX, Y or Z, or any combination thereof.

In some embodiments, certain operations, acts, events, or functions ofany of the algorithms described herein can be performed in a differentsequence, can be added, merged, or left out altogether (e.g., not allare necessary for the practice of the algorithms). In certainembodiments, operations, acts, functions, or events can be performedconcurrently, e.g., through multi-threaded processing, interruptprocessing, or multiple processors or processor cores or on otherparallel architectures, rather than sequentially.

Systems and modules described herein may comprise software, firmware,hardware, or any combination(s) of software, firmware, or hardwaresuitable for the purposes described. Software and other modules mayreside and execute on servers, workstations, personal computers,computerized tablets, PDAs, and other computing devices suitable for thepurposes described herein. Software and other modules may be accessiblevia local computer memory, via a network, via a browser, or via othermeans suitable for the purposes described herein. Data structuresdescribed herein may comprise computer files, variables, programmingarrays, programming structures, or any electronic information storageschemes or methods, or any combinations thereof, suitable for thepurposes described herein. User interface elements described herein maycomprise elements from graphical user interfaces, interactive voiceresponse, command line interfaces, and other suitable interfaces.

Further, processing of the various components of the illustrated systemscan be distributed across multiple machines, networks, and othercomputing resources. Two or more components of a system can be combinedinto fewer components. Various components of the illustrated systems canbe implemented in one or more virtual machines, rather than in dedicatedcomputer hardware systems and/or computing devices. Likewise, the datarepositories shown can represent physical and/or logical data storage,including, e.g., storage area networks or other distributed storagesystems. Moreover, in some embodiments the connections between thecomponents shown represent possible paths of data flow, rather thanactual connections between hardware. While some examples of possibleconnections are shown, any of the subset of the components shown cancommunicate with any other subset of components in variousimplementations.

Embodiments are also described above with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products. Each block of the flow chart illustrationsand/or block diagrams, and combinations of blocks in the flow chartillustrations and/or block diagrams, may be implemented by computerprogram instructions. Such instructions may be provided to a processorof a general purpose computer, special purpose computer,specially-equipped computer (e.g., comprising a high-performancedatabase server, a graphics subsystem, etc.) or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor(s) of the computer or other programmabledata processing apparatus, create means for implementing the actsspecified in the flow chart and/or block diagram block or blocks. Thesecomputer program instructions may also be stored in a non-transitorycomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to operate in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the acts specified in the flow chart and/or blockdiagram block or blocks. The computer program instructions may also beloaded to a computing device or other programmable data processingapparatus to cause operations to be performed on the computing device orother programmable apparatus to produce a computer implemented processsuch that the instructions which execute on the computing device orother programmable apparatus provide steps for implementing the actsspecified in the flow chart and/or block diagram block or blocks.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the invention can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further implementations of theinvention. These and other changes can be made to the invention in lightof the above Detailed Description. While the above description describescertain examples of the invention, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the invention disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the invention under theclaims.

To reduce the number of claims, certain aspects of the invention arepresented below in certain claim forms, but the applicant contemplatesother aspects of the invention in any number of claim forms. Forexample, while only one aspect of the invention is recited as ameans-plus-function claim under 35 U.S.C sec. 112(f) (AIA), otheraspects may likewise be embodied as a means-plus-function claim, or inother forms, such as being embodied in a computer-readable medium. Anyclaims intended to be treated under 35 U.S.C. § 112(f) will begin withthe words “means for,” but use of the term “for” in any other context isnot intended to invoke treatment under 35 U.S.C. § 112(f). Accordingly,the applicant reserves the right to pursue additional claims afterfiling this application, in either this application or in a continuingapplication.

1. (canceled)
 2. A computer-implemented method, comprising: identifying,by a first computing system, a command associated with a secondcomputing system from a plurality of commands of a distributed executionmodel to execute at least a portion of a query; generating a datastructure in a restricted computing environment based on the command;and executing one or more computer executable instructions based on thedata structure.
 3. The computer-implemented method of claim 2, whereinthe command comprises an untrusted command.
 4. The computer-implementedmethod of claim 2, wherein a command associated with the first computingsystem comprises a trusted command.
 5. The computer-implemented methodof claim 2, further comprising receiving the distributed executionmodel.
 6. The computer-implemented method of claim 2, wherein theplurality of commands correspond to a plurality of commands of thequery.
 7. The computer-implemented method of claim 2, further comprisingadding an identifier associated with the data structure to thedistributed execution model.
 8. The computer-implemented method of claim2, wherein identifying the command comprises identifying the commandbased on a syntax model.
 9. The computer-implemented method of claim 8,wherein the syntax model comprises an abstract syntax tree.
 10. Thecomputer-implemented method of claim 8, wherein the syntax modelcomprises a plurality of command nodes corresponding to a plurality ofcommands of the query.
 11. The computer-implemented method of claim 8,wherein the syntax model identifies one or more trusted commands and oneor more untrusted commands.
 12. The computer-implemented method of claim2, wherein the distributed execution model comprises a distributedacyclic graph.
 13. The computer-implemented method of claim 2, whereinthe command is associated with libraries and dependencies that are notknown by a system executing the at least a portion of the query.
 14. Thecomputer-implemented method of claim 2, wherein each of the plurality ofcommands is associated with one or more computation operations.
 15. Thecomputer-implemented method of claim 2, wherein the plurality ofcommands include a trusted command that is associated with libraries anddependencies that are known by a system executing the at least a portionof the query.
 16. The computer-implemented method of claim 2, furthercomprising instantiating the restricted computing environment togenerate the data structure.
 17. The computer-implemented method ofclaim 2, further comprising communicating data associated with thecommand to the restricted computing environment to generate the datastructure.
 18. The computer-implemented method of claim 2, whereingenerating the data structure comprises: communicating data associatedwith the command to the restricted computing environment using anexternal transformer; and receiving the data structure from therestricted computing environment using the external transformer.
 19. Thecomputer-implemented method of claim 2, wherein generating the datastructure comprises communicating data associated with the command tothe restricted computing environment, wherein the data associated withthe command includes a filename and a file location associated with afile, wherein the restricted computing environment uses the filename andthe file location to identify the file and uses the file to generate thedata structure.
 20. A first computing system, comprising: memory; andone or more processing devices coupled to the memory and configured to:identify, a command associated with a second computing system from aplurality of commands of a distributed execution model to execute atleast a portion of a query; generate a data structure in a restrictedcomputing environment based on the command; and execute one or morecomputer executable instructions based on the data structure.
 21. Anon-transitory computer readable media comprising computer-executableinstructions that, when executed by a first computing system, cause thefirst computing system to: identify, a command associated with a secondcomputing system from a plurality of commands of a distributed executionmodel to execute at least a portion of a query; generate a datastructure in a restricted computing environment based on the command;and execute one or more computer executable instructions based on thedata structure.